Reach Your Marketing KPIs With the Scale & Efficiency Metrics Framework

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Marketers spend a lot of time sharing and defending the results of campaigns and activities. Stakeholders and leaders constantly ask: “What results did this drive?” “Where can we cut our budget?” “What can we optimize?”

To answer these questions, marketers must navigate metrics from vastly different sources that span tools, dashboards, and even authors (a custom report an agency composes may vary dramatically from the tables a chief marketing officer pulls). Each source will emphasize different data and calculations — impressions, engagement rate, click-through rate, return on investment, and more. Misunderstanding or misinterpreting numbers at any stage can have a severe impact on your business.

That’s where the Scale and Efficiency Metrics Framework — one of many tools you can use to plan and optimize marketing campaigns — comes in. There are two types of metrics typically used by marketers: scale metrics, which indicate sums or volumes (e.g., number of website visitors), and efficiency metrics, which indicate rates or ratios (e.g., return on investment). Knowing the value of each type of metric helps you think about how to scale and optimize your marketing activities.

The premise of the framework is that all scale metrics fall into one of four buckets:

  • Cost measures how much is spent on a campaign, including agency or ad fees.
  • Reach measures the number of people contacted through impressions, visitors, video views, and so on.
  • Response determines whether or not audience members take the actions you want them to take through metrics like clicks, swipes, or completed views of a video.
  • Revenue measures the amount of money made, as in total revenue or lifetime customer value.

An efficiency metric is the ratio of two of those buckets (i.e., one bucket divided by another), and each one tells you something different. By breaking down reports into these buckets and metrics, it’ll be easier to compare how you did across different channels and prioritize which channels to invest in further.

You can use the Scale and Efficiency Metrics Framework to:

  1. Compare metrics across different channels.
  2. Understand the impact marketing efforts have on growth.
  3. Determine the cost and efficiency of growth.

Now, let’s take a closer look at some of these metrics and how you can leverage them to optimize your marketing strategy.

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How to Use Scale and Efficiency Metrics Together

Armed with a clear metrics strategy, marketers can create and refine campaigns that focus on reaching a specific outcome and make smarter business decisions when allocating or requesting budget. Metrics are also powerful tools for persuading stakeholders to devote funds toward future marketing efforts.

As we mentioned previously, scale metrics measure volume. They are useful in understanding figures like how many people a campaign reached, how much money users spent on your product, or how much you spent on ads. They are also helpful in analyzing the success of a campaign, can be used to validate hypotheses, and are an effective way to measure growth over time.

At their core, efficiency metrics are numbers that indicate effectiveness. These metrics are always rates or ratios and are often expressed as a percentage.

5 Key Efficiency Metrics

Cost per Reach

Wondering how far your money stretches to reach your audience? Cost-per-reach metrics indicate the cost-effectiveness of a media channel or partner. They’re helpful in guiding how much money to invest in the various platforms on which your campaign runs.

The most common cost-per-reach ratio is cost per mille (CPM), or the cost per thousand impressions. This is a useful metric to have when planning the budget required to achieve a target reach. A social media marketer might use it to gauge the success of a Facebook ad in a brand awareness campaign.

The formula for calculating CPM is Cost ÷ Impressions x 1,000. For example, if you buy 50,000 impressions for $250, your CPM is $250 ÷ 50,000 x 1,000, or $5.

Response per Reach

Response-per-reach metrics help determine what percentage of audience members who could have taken a desired action actually did. For example, of all the people to whom you showed a banner ad, how many clicked on it?

Prevalent response-per-reach metrics include:

  • Engagement rate, which indicates the percentage of viewers who see an ad and interact with it in any way. It’s typically used for interactive media and social ads. The most common engagement rate formula is Interactions ÷ Impressions x 100. For example, if you had a social ad that generated 100 impressions, three likes, and five shares, your engagement rate would be 8 ÷ 100 x 100, or 8%. However, it’s important to note that there are multiple ways to look at engagement rate. Some marketers use reach or followers as the denominator in place of impressions.
  • Click-through rate (CTR), which is the percentage of people who were exposed to an ad and then clicked on it. The formula for calculating CTR is Clicks ÷ Impressions x 100. For example, if you ran a Facebook News Feed ad that generated 3,000 impressions and received 50 clicks, your CTR would be 50 ÷ 3,000 x 100, or 1.67%.
  • Conversion rate, which is the percentage of people who had the opportunity to complete an action you defined — and did.

This metric requires context to pinpoint the best version of the formula to use. To calculate the conversion rate for prospects who clicked on an ad, or what’s known as a post-click conversion rate, the formula would be Conversions ÷ Clicks x 100. To measure the percentage of site visitors who convert, regardless of traffic source, your visitor conversion rate would be Conversions ÷ Visitors x 100. Adjust the denominator based on the context that makes sense for your campaign.

Cost per Response

Marketers can pinpoint which channel or tactic is most cost-efficient in driving results with cost-per-response metrics. Typically, these metrics are measured as a ratio of cost per (X), or CP(X), with X indicating an action.

Here are a few examples of how they may be calculated across different contexts:

  • Search: cost per click
  • eCommerce: cost per order
  • Apps: cost per install or cost per download
  • B2B: cost per lead
  • Video: cost per completed view

To calculate any of these metrics, divide total ad spend by number of X, where X equals leads, clicks, orders, or whatever you are tracking. For example, if you spent $10,000 to acquire 200 leads, your cost per lead is 10,000 ÷ 200 or $50.

Be mindful of quality when focusing on CP(X) metrics. For instance, if you’re comparing cost per click across multiple platforms, one may clearly outperform the other. But, when you dig deeper, you may see that the lower performer actually drove more high-quality leads.

Revenue per Response

Cost-per-response performance metrics help determine which tactics and campaigns are most cost-effective — but their uses are limited on their own. If you don’t know how much those “responses” are worth in terms of revenue, you won’t know how much expense is too much.

This is where revenue-per-response metrics come in.

If a Facebook campaign runs at a $30 cost per download (CPD) and a search campaign runs at a $50 CPD, your instinct might be to stop spending on search and move that budget to Facebook. However, you won’t really know whether or not you need to stop spending unless you know how much an app download is worth to your business. If a download results in $51 of revenue (a $1 profit per download), it may still be worth running search ads to reach a wider audience than you could on Facebook alone.

In other words, you won’t always want to go with the lowest “cost per” available, as scale comes into play.

One common revenue-per-response metric is average order value (AOV) — the average size of a purchase on a website or app. For retail and eCommerce businesses, average order value will be a key metric.

The formula is Revenue ÷ Number of Orders. So, if you made $10,000 from 50 orders, your AOV would be 10,000 ÷ 50, or $200.

Return on Investment

Ah, the holy grail of marketing: The ability to say exactly how much incremental revenue was generated by the marketing campaign! You’ll often hear ROI tossed around in discussions surrounding the overall efficacy of a marketing strategy and the bottom line.

The formula for return on investment (ROI) is expressed as a percentage and calculated as (Revenue – Cost) ÷ Cost x 100. For instance, if you generated $200 in revenue from a Facebook ad and you spent $125 on it, then your ROI would be (200 – 125) ÷ 125 x 100, or 60%.

Sometimes, return on ad spend (ROAS) is used instead of ROI. ROAS does not subtract the cost from the numerator. The formula here is simply Revenue ÷ Cost x 100.

Applying the Scale and Efficiency Metrics Framework

Now that you’ve grasped the basic principles behind this framework, it’s time to apply them to a marketing campaign.

Let’s say your plan involves Facebook and YouTube campaigns driven by your in-house team, plus a programmatic display campaign implemented by an outside agency. For each of those channels, you want to determine:

  • What you spent.
  • How many people you reached.
  • What kind of response you got from that reach.
  • Whether there was a direct revenue outcome.

The metrics outlined in this framework help you answer those questions for each channel. Once you’ve calculated and compared the results, you may discover that your cost per acquisition is much lower on Facebook than on other channels. Based on this information, you may want to investigate why this campaign is performing so much better than other channels. (Is it the creative? The targeting? Or were the viewers on Facebook also exposed to other channels?) This can help you make optimization decisions that will improve your overall ROI.

More Ways to Organize and Optimize Your Marketing Strategy

The Scale and Efficiency Metrics Framework is just one of many tools you can use to organize goals, prioritize approaches, create effective campaigns, determine which data to focus on, and more. In our free, exclusive paper, Campaign Essentials, dive into three more valuable frameworks commonly used throughout General Assembly’s digital marketing programs. Each framework serves a different purpose in focusing, planning, executing, and optimizing your marketing campaigns.

Dive into the tactics that drive successful marketing campaigns through our part-time 10-week or 1-week accelerated Digital Marketing course, on our global campuses or online. Learn practical skills in short-form workshops and bootcamps, connect with others in the field at our exclusive campus events, or get an overview of the field in a free livestream. For teams, strengthen your marketing operations by assess your marketers’ skills, identifying growth opportunities, and closing your skills gaps.

Leveraging the Paid, Owned, and Earned Media Framework

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Whether they’re working on a paid social media campaign, crafting copy for a website, or landing coverage in a magazine or blog, many marketing managers share one goal: to raise brand awareness. However, with multiple team members creating countless content assets across several digital channels, processes can get hectic — and inefficient — quickly.

An effective marketing operation requires that everyone on the team takes a holistic view of how media tactics work together. Media tactics fall into three main buckets:

  1. Paid: Media that a marketer pays for, such as display advertising, pay per click (PPC), paid influencers, and retargeting.
  2. Owned: Media from owned properties such as websites, mobile apps, email, and social media platforms.
  3. Earned: Shares or mentions outside of owned channels, such as blog links, news articles, and customer testimonials (negative reviews included).

Most organizations leverage media in all three categories, and each channel often correlates with the responsibilities of different roles and teams. Some channels, like social media, influencer marketing, and content, may intersect with all three buckets.

When team members are aligned on the interplay between paid, earned, and owned media, they can work to tell a cohesive, consistent story across multiple channels. At the same time, they can optimize their contributions independently by thinking about how their work impacts the rest of the organization.

Juggling so many channels and developing a strategy that includes owned, paid, and earned media can be daunting. It requires organization — and that’s where the Paid, Owned, and Earned Media Framework, one of many tools you can use to organize your marketing efforts, comes in.

This framework provides a roadmap for considering media options and choosing which tactics to pursue. Leverage it to:

  1. Understand and assess the value of different channels for your brand.
  2. Organize how you utilize these channels.
  3. Evaluate how different channels amplify one another.
  4. Get a better understanding of your colleagues’ roles.
  5. Align teams across media tactics to create a converged campaign.

Let’s take a deeper dive into the three types of media.

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Paid Media

Paid media today include both traditional and new types:

  • Banner, video, social, and native advertising
  • Paid search
  • Paid influencers
  • Affiliates
  • TV
  • Print
  • Out-of-home

Thanks to the data at the heart of new media, the conversation goes beyond reach to include engagement and relevance. Paid media are often the most expensive digital marketing tactics, but they offer more immediate and predictable reach to a target audience.

Paid campaigns are usually classified in one of two ways:

  1. A brand campaign prioritizes reach. The goal is to share your message with a target audience, raising brand awareness and intent to purchase.
  2. A direct response campaign is meant to drive an action. These typically offer a promotion and a clear call to action (CTA) to try to incentivize the user to complete a conversion.

Thanks to the evolution of creative formats, both goals can be achieved in a single ad that catches a user’s attention, educates them about a product, and drives them through a conversion. For example, Facebook’s carousel format, when used with its Lead Generation objective, allows users to enter their email address directly into an ad unit. This lets a marketer combine both brand awareness and direct response into a single ad.

Owned Media

Owned media channels are those that are fully under a brand’s control. They can be designed, updated, and shared at the company’s discretion.

Websites are arguably the most important owned assets, as a major goal of nearly all other digital marketing channels is to drive traffic to company websites where visitors can then convert. Other key properties include mobile apps, eCommerce sites, content, email lists, direct mail, SMS/messaging lists, and branded social channels.

A company’s physical locations and hosted events are also owned channels, as businesses have control over what happens there.

Owned media channels give you the opportunity to educate and entertain your customers, as well as create a seamless customer experience. For many individuals like email marketing managers, brand managers, and SEO specialists, optimizing owned media channels is a full-time job.

Earned Media

Earned media come from sources outside of your organization, often in the form of word-of-mouth recommendations. Studies show that people find recommendations from people they know to be more trustworthy than content coming directly from brands, and no amount of paid advertising can make up for a lack of valuable earned media.

Common sources of earned media include social media and content marketing shares, unpaid influencers, public relations, reviews, and testimonials. This type of marketing can be very effective but tends to require a longer-term effort than paid and owned media.

It’s also important to note that earned media are the least in our control. We can put effort into driving press coverage or influencer shoutouts, but we can rarely dictate what people say about our brand.

How to Plan a Converged Media Campaign

The most effective marketing strategies combine paid, owned, and earned media to create an impact that’s greater than the sum of its parts. Moreover, the combination that’s best for your brand will be one that’s uniquely tailored to your size, budget, resources, and existing reach.

When you’re planning a campaign, use the worksheets found in our free Campaign Essentials guide to ensure that all stakeholders and teams are aligned on how they’re contributing to the larger end goal(s). As you’ll see, many of these questions incorporate themes and strategies covered in earlier frameworks, and we encourage you to keep your SMART objectives and KPIs in mind.

More Tools to Master Your Marketing Operations

The Paid, Owned, and Earned Media framework is just one of many tools you can use to organize goals, prioritize approaches, create effective campaigns, determine which data to focus on, and more. In our free, exclusive paper, Campaign Essentials, dive into three more valuable frameworks commonly used throughout General Assembly’s digital marketing programs. Each framework serves a different purpose in focusing, planning, executing, and optimizing your marketing campaigns.

Dive into the tactics that drive successful marketing campaigns through our part-time 10-week or 1-week accelerated Digital Marketing course, on our global campuses or online. Learn practical skills in short-form workshops and bootcamps, connect with others in the field at our exclusive campus events, or get an overview of the field in a free livestream. For teams, strengthen your marketing operations by assess your marketers’ skills, identifying growth opportunities, and closing your skills gaps.

Master Your Content Marketing Strategy With the Content Honeycomb

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Content marketing encompasses the creation and distribution of content that aims to help a specific target customer progress through their journey toward a business conversion.

For your brand’s content to be noteworthy, it has to provide value to the user. The Content Honeycomb is General Assembly’s framework — modeled after information architecture pioneer Peter Morville’s widely used User Experience Honeycomb — for helping you generate, evaluate, and push content marketing strategies that make your brand stand out. It’s one of many valuable tools you can use to plan, organize, and optimize your marketing efforts.

The Content Honeycomb posits that high-value content possesses certain key characteristics. Some (or all) of it should be participatory, entertaining, helpful, educational, meaningful, and/or unique.

If you look at any content success story, it probably ticks the box for at least two or three of these characteristics. You should aim to do the same.

The Content Honeycomb is a great tool for evaluating content, whether it’s created in-house or by an outside agency. As you review each piece of content, ask which boxes it ticks off. If it’s helpful, can you also make it entertaining? If it’s educational, can it also be participatory? In this regard, the framework is extremely valuable in helping to articulate what’s missing from any given content campaign.

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High-Value Content Breakdown

Marketers with a deep understanding of content strategy are more in tune with how their customers feel, what they say, and what they hear. They listen and tailor their efforts according to what their audience really wants — and these efforts translate into results.

What makes for a strong content strategy? Specific characteristics, like “participatory” and “meaningful,” lie at the core of the Content Honeycomb, and crafting material that embodies those terms requires thoughtfulness and detail. Let’s break down each Honeycomb component and explore how you can begin putting it to work.

Meaningful

Meaningful content connects with an audience on a deeper emotional, intellectual, or philosophical level. This content isn’t just about being warm and fuzzy — it’s a business differentiator.

To create meaningful content:

  • Start conversations on social media about resonant topics.
  • Conduct interviews with thought leaders that reveal insights that can improve readers’ lives.
  • Showcase social impact stories that highlight your brand’s commitment to bettering communities and advancing worthy causes.
  • Share stories of people who have been positively impacted by your brand.

I can publish a post on my food review app’s blog that highlights how local restaurants partner with community gardens to incorporate fresh, organic ingredients into their menus.

Educational

Each day, customers search the internet to learn about their interests. They want to go behind the scenes, find out what’s new, and get inspired. Educational content informs an audience about topics that are relevant to a company’s goods, services, or values.

To create educational content:

  • Craft tutorials and how-tos on skills related to your product.
  • Publish slide decks, white papers, or blog posts with helpful information on current trends.
  • Conduct webinars or live “ask me anything” (AMA) broadcasts to share insights from your business’s thought leaders.
  • Condense useful facts into shareable infographics.

I will partner with a chef to produce a cooking tutorial video and host it on my app.

Helpful

Helpful content is just that — it makes things easier for customers, whether it’s a tax calculator and guide to use throughout the season, or simply an FAQ series related to a product.

To create helpful content:

  • Build apps and tools that solve problems for your customers.
  • Share resources and toolkits that assist people in using your product or service to its full potential.
  • Publish white papers that provide insight into your readers’ lives and provide actionable advice.
  • Address common questions with FAQs.

I will create a “traveling foodie’s dictionary” that translates common terms found on regional menus.

Participatory

Participatory content aims to make customers part of a brand story. It inspires people to act, whether they’re engaging in a webinar’s open-chat forum or contributing to a community LinkedIn Group.

To create participatory content:

  • Leverage tools like live video to host a forum in which customers can interact with or add to the content as you’re creating it. Create live, offline experiences that customers can take part in.
  • Run contests and competitions that invite users to create and share original content.
  • Use quizzes and polls to invite people to find out more about themselves — and your brand.

We’ll run a virtual “scavenger hunt” in which users can “find” ingredients at restaurants they review in exchange for points that can be redeemed for dining discounts.

Entertaining

There’s an old adage that suggests people remember how you make them feel more than they remember what you say or do. This also applies in the world of marketing and is the best way to approach creating entertaining content. Marketers can humanize their brands through content that resonates with strong emotions to develop deeper connections with their audiences.

To create entertaining content:

  • Share entertaining photos, videos, or even animated GIFs that connect your brand personality, key messaging, and target audience.
  • When it works, consider bringing humor into the equation.
  • Engage in brand storytelling, experimenting across media formats — videos, slideshares, podcasts, articles, etc.
  • Leverage influencers to create and share original branded content.

I will tweet out trending GIFs that pair well with quotes from user reviews.

Unique

Today’s consumers are met with a constant deluge of new content, from their email inboxes to their social media feeds. Your content not only needs to be fresh and different — it also has to stand out. Effective campaigns are often based on a deep understanding of a specific customer and what matters to them. They break through the clutter of dull “brand speak” and talk to customers in a way that’s relatable — and unique.

To create unique content:

  • Look for content your customers are already generating that’s related to your brand, and play off of it.
  • Offer experiences — either online or in person — that cannot be had anywhere else.
  • Start with the problem your product solves. Reference the work of other leaders in the field or create content in partnership with them to provide original, cross-industry perspectives on your customer’s core needs.

I will compile and share neighborhood-specific restaurant guides by aggregating reviews that users have written on my app.

A strong content strategy should extend consistently across all marketing functions, as every platform and channel is an opportunity to galvanize your audiences and introduce them to your brand. To use content to its full potential across paid, owned, and earned media, engage in ongoing, cross-team brainstorming and keep the Content Honeycomb in mind. By following this framework, your content will make strides in driving profit and elevating the profile of your brand.

More Tools to Hone Your Marketing Tactics

The Content Honeycomb is just one of many tools you can use to organize goals, prioritize approaches, create effective campaigns, determine which data to focus on, and more. In our free, exclusive paper, Campaign Essentials, dive into three more valuable frameworks commonly used in General Assembly’s digital marketing programs. Each framework serves a different purpose in focusing, planning, executing, and optimizing your marketing campaigns.

Dive into the tactics that drive successful marketing campaigns through our part-time 10-week or 1-week accelerated Digital Marketing course, on our global campuses or online. Learn practical skills in short-form workshops and bootcamps, connect with others in the field at our exclusive campus events, or get an overview of the field in a free livestream. For teams, strengthen your marketing operations by assess your marketers’ skills, identifying growth opportunities, and closing your skills gaps.

Set Smart Marketing Objectives With the Objective-First Framework

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If you’ve ever watched Mad Men, the acclaimed TV drama about the 1960s heyday of Madison Avenue ad agencies, you have an inkling of how marketing worked before digital media and the internet.

Back then, businesses:

  • Identified their target markets and customer value propositions.
  • Crafted creative messages to inspire the audience to try their products.
  • Launched a campaign on TV, on radio, and in print, and…
  • Waited weeks or even months to find out whether or not it worked.

This approach reached potential customers at the top of the marketing funnel, at what’s known as the awareness stage. It was challenging for traditional marketers to target certain demographics and strategically serve different ads to specific audiences.

Today, however, marketers can reach people much further along in the funnel. Digital platforms like Google Analytics, Facebook, and SailThru provide detailed insight into consumer behavior at pivotal points such as the consideration and conversion stages, when people are ready to take action. There are also countless content formats that marketers can leverage across these platforms to influence behavior. The vast range of opportunities to reach and galvanize audiences makes for more effective marketing campaigns — but also more complexity for the people who plan them.

That’s where frameworks come in — tools that help marketers organize goals, prioritize approaches, create marketing plans, and more. Here we’ll tackle the Objective-First Framework, which will help you set laser-focused goals for any campaign. (For more frameworks to plan, optimize, and measure your marketing efforts, download our free guide, Campaign Essentials.)

To take advantage of all the tools and data available, marketers must be crystal clear on what they and their business are trying to accomplish, and why. Launching media plans across channels without truly understanding key objectives can lead to lackluster results that compromise the brand — and the bottom line.

Set yourself, your team, and your business up for success by establishing explicit marketing objectives and a well-defined path for achieving them.

The Objective-First Framework offers a streamlined approach to setting goals, drawing conclusions, and analyzing channels. It takes a lot of ambiguity out of crafting objectives and aligns stakeholders on what defines success. This powerful tool helps you:

  1.  Structure marketing efforts.
  2. Share plans and results.
  3. Use marketing resources wisely.
  4. Discern what data is and isn’t important.
  5. Establish a common goal and ensure that all stakeholders are aligned.

The Objective-First Framework can be implemented at any level of your marketing organization — individuals can use it to keep their own goals on track, and teams can use it to pursue big-picture targets. The framework helps you outline goals and hypothesize, execute, and measure results, which means a quicker path to success.

How to Build a Strong, SMART Marketing Objective

As the name of this framework implies, choosing your objective is the most essential step in planning a marketing strategy and campaigns across any channel. A strong marketing objective will answer two critical questions:

  1. What perception or behavior do you want to change in your customers?
  2. What will changing this perception or behavior do for your business?

To set up an objective, first consider the following questions:

  • What do I or my team specifically want to achieve?
  • Why is this goal important to achieve?
  • By when do I need to achieve this goal?
  • What defines success?

Once you answer these questions, you can determine whether or not your objective is SMART: specific, measurable, attainable, realistic, and time-bound. Just like the acronym suggests, a SMART objective is well thought out and crafted with consideration. It keeps you focused on the path to reaching your goal and helps you avoid logistical or strategic pitfalls.

Here’s a breakdown of the qualities that define SMART objectives.

Specific

A good objective should be as specific as possible; this will help you to measure your progress toward reaching it. If your objective can be interpreted in several different ways, it may not be specific enough.

Let’s say you wanted to recruit users for a food review app. Your objective might be, “Attract 200 new users this month.” However, without stating that you want those users to be active contributors to your community, your team might offer a one-time sign-up reward. This may get 200 new users, but they will likely be bargain-hunters who won’t contribute to the community… or return to the app. Make this objective more specific by defining the behaviors users need to take in the app before they can be counted toward your goal.

Measurable

Could stakeholders disagree on whether or not your objective was achieved? If so, it’s not sufficiently measurable. To make success as unambiguous as possible, think of hard numbers or objectives with “yes or no” answers that remove guesswork from analysis. For example, if your objective is, “Attract 200 new users who will write at least two food reviews in their first month using the app,” you’ve defined a clear “yes” or “no” question with a quantifiable, measurable answer.

People across your organization should also be aligned on the tool(s) you’ll use as a source of measurement — for example, the profit and loss report, a client survey, or sales reports. This establishes a shared vocabulary and ensures that everyone is on the same page (literally) when looking at metrics.

Attainable

Choose an objective that you know can be achieved but is not guaranteed. This will keep you motivated and creative. If your objective is too easily attainable, there’s no challenge in it and it may not impact broader business objectives in a significant way. On the other hand, if your objective is completely unrealistic, you risk wasting resources, frustrating leaders and teammates, and possibly failing the business.

Realistic

Don’t set objectives that rely heavily on something that’s outside of your influence or lie dramatically beyond benchmark performance. If your plan requires technologies you don’t have (or don’t exist!), exceeds your budget, or leans on talent that isn’t available, your chances of succeeding will be greatly limited.

Time-Bound

Set target dates and key milestones to keep things on track. A realistic time frame provides a finish line to look forward to and creates a sense of urgency for accomplishing the goal. Milestones help organize and streamline key steps in a campaign and hold teams and stakeholders accountable for different components of the project.

Applying the Objective-First Framework to Your SMART Objective

Now that you’ve crafted a SMART objective, it’s time to work through the rest of the Objective-First Framework. In this section, we’ll outline each of the framework’s six steps and their role in driving a successful marketing campaign. We’ve identified the main goal of each step and provided a few key questions you can ask to guide your progress.

Set Objectives

Set a SMART objective that describes why you are running the campaign and what you hope to accomplish. Key questions to answer include: What customer behavior are you trying to change? What will that do for the business?

My objective is to attract 200 new engaged users to my food review app in the next 12 weeks. Engagement will be defined as a user posting two reviews in their first month using the app. This objective will increase engagement and community involvement on the app, creating a more attractive package for advertisers. This will boost the app’s revenue.

Define KPIs

Determine the key performance indicators (KPIs) you’ll use to evaluate the success of your campaign. KPIs are the metrics that you identify as most important for tracking performance against your stated objective. All KPIs are metrics, but not all metrics are KPIs. Consider: What are the top one to three metrics that address, “Did we reach our objective?”?

I’m going to track number of new users, how many reviews each new user posts on the app, and when they post them. My top metrics will be 1) number of new users — defined by creation of new accounts — between April 2 and June 25, and 2) number of reviews posted by users who joined between April 2 and June 25 within first month after app download.

Design Tactics

Determine how to reach your target customer by asking yourself: Where does your target customer spend time online? What devices, websites, and apps are they using? What motivates them?

I’m going to launch an Instagram ad campaign targeting users between 24–32 years old who are food enthusiasts and use similar food apps. My target customer spends a lot of time eating at restaurants, posting and looking at food photos on Instagram. They’re motivated by trying the trendiest new dishes around the city and showing off what they ate.

Execute Campaign

Put your tactics into action in the channels you believe will be most effective for your campaign, based on your research conducted in Step 3. Then, identify the resources and team members you need to execute this campaign.

The Instagram campaign will cost $250. I need the Creative team to choose three images and write copy for the Instagram post, plus create a landing page to compel visitors to download the app. I’ll also need the Partnerships team to create a tracking URL to which potential users will be directed.

Measure Outcomes

Measure and analyze your performance as it occurs. This will help gauge the health of your campaign along the way. A helpful question to ask is: What metrics tell you how you can improve performance?

In the first two weeks of the Instagram campaign, 5,000 people visited the URL and 250 of them downloaded the app. Fifty-five of those people published one review in their first week after downloading the app. The fact that 5,000 people clicked the link from our Instagram page but only 250 of them downloaded the app suggests that the content on the page to which users were directed wasn’t sufficiently compelling. I need to get more people who click on the Instagram ad to actually download the app.

Optimize Results

Use your results to inform iterations on the campaign that hopefully boost performance. It’s likely you’ll find variables you can alter in your campaign that may move the needle on your goals.

Because I suspect the issue is the landing page, I could create an alternate version of it with different images and copy, then perform an A/B test to compare download rates between the two pages.

More Strategies to Drive Winning Digital Marketing Campaigns

The Objective-First Framework is just one of many tools to help marketers organize goals, prioritize approaches, create effective campaigns, determine which data to focus on, and more. In our free, exclusive paper, Campaign Essentials, dive into three more valuable frameworks commonly used throughout General Assembly’s digital marketing programs.  Each framework serves a different purpose in focusing, planning, executing, and optimizing your marketing campaigns.

Dive into the tactics that drive successful marketing campaigns through our part-time 10-week or 1-week accelerated Digital Marketing course, on our global campuses or online. Learn practical skills in short-form workshops and bootcamps, connect with others in the field at our exclusive campus events, or get an overview of the field in a free livestream. For teams, strengthen your marketing operations by assessing your marketers’ skills, identifying growth opportunities, and closing your skills gaps.

Digital Marketing Campaign Essentials

Boost your skills and launch campaigns that drive real impact with this exclusive guide.

Download the Paper

6 Tips on How to Get a Job at a Startup

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How to narrow your focus, get a leg up on the competition, and look like the most prepared person in the room.

A job-search thesis is a great tool to tell people what you’re looking for in a job.

The following is an adapted excerpt from How to Get a Job at a Startup, an exclusive General Assembly eBook by startup founder and former GA leader Matt Cynamon.

Working for a startup company can be one of the most challenging, exhilarating, sometimes heartbreaking, and oftentimes fulfilling journeys of your life. But wanting in and breaking into this competitive industry are two different things. Landing an opportunity at a startup is about more than luck. There are terms to learn, steps to take, and a skill set to grow from to make you a candidate who stands out from an established crowd.

Whether you’re a recent college graduate, someone with 10 years of executive-level experience, recently completed a career accelerator program, or are just making a jump from a more traditional work background, there is a pathway to a dream job at a startup for everyone. While there’s no foolproof method for landing a job, we’ve compiled six proven tips that can help you narrow your focus, get a leg up on the competition, and look like the most prepared person in the room.

1. People can get you further than job boards.

One of the nice surprises about the startup businesses is how supportive and helpful some of the people are. In every city, leaders in grassroots startup communities host events, give educational talks, make introductions, and offer advice. These individuals can serve as your early guides as you start out on your journey.

If you’re just breaking into the startup world, you may not have a strong network to draw upon. That’s OK. Go to events, meet people, and listen. As a new entrant into the community you might feel like you have little to offer in return, but one of the biggest favors you can do for someone is just ask them questions about their work. Don’t be too forceful, but where appropriate, invite people for a coffee. It may seem intuitive, but being generally interested in others and what they do will help you foster relationships that aren’t only valuable, but fulfilling.

When it comes time for you to start applying, warm introductions from someone within the community will go much further than a resume submitted on a job board. Founders often cite hiring as the biggest obstacle to successfully growing their company. It’s a timely and difficult process that they love to circumvent with a nice, warm introduction to top talent (aka you).

One of the most common mistakes people make when trying to get introductions is assuming that if people don’t get back to you, hope is lost. Be prepared for repeated failure. Ninety percent of people will say they want to help you. Ten percent actually will. Why most people don’t follow through is due to a variety of factors, but just know it’s rarely about you. If you go into every conversation with this attitude, you will more easily be able to sustain your energy when your inbox sounds like crickets.

2. Polish your elevator pitch with a job-search thesis.

We’re living in an age of self-driving cars, private spaceships, artificial intelligence, augmented reality, on-demand everything — and startups often lie at the center of these innovations. It’s completely normal for someone starting on their journey to want to be a part of all of it. While you will encounter many people who are willing to help you in your job hunt, you need to make it easy for them to do so. To that end, nothing will get you further than clarity and focus.

When you tell people what you are looking for, you want them to think, “I know who you should talk to.” The easiest way to get there is to distill what you’re looking for into three distinct points. We call this a job-search thesis.

The best job-search thesis will contain:

  • Your desired company size.
  • Your preferred industry.
  • Your desired role.

For example, if you can tell someone at a cocktail party, “I want to work as a product manager at post-Series A company in the fashion industry,” there’s a good chance they’ll remember you the next time they hear about a PM role at a company that makes smart athletic gear. Speaking about yourself with that level of specificity will instantly make connections in the mind of whomever you find yourself talking to.

3. Got experience? Great. Not so much? Then make it.

If you are moving into the startup world from a career in a different industry or type of role, make sure to play up your relevant experience. If you feel like your job title really doesn’t translate to the position for which you’re applying, break apart the components of your current role into the factors that would be relevant at a startup. For example, if you were a lawyer then you likely have strong attention to detail, analytical problem-solving skills, an ability to explain complex problems to many stakeholders, a strong work ethic, and a history of achievement. These are all things startup founders would want out of product management. This exercise is especially important for more senior individuals trying to move into the startup world.

Of course, you don’t have to rely only on your previous experience — the best candidates never do. Fortunately, the rules around experience have shifted and there are ways for you to start developing skills within a given field even if you’ve never worked in that field before.

Let’s say you’re really interested in doing digital marketing for a fashion tech company. For less than $50 you can start running Facebook advertisements for a friend’s T-shirt website, cultivating skills in running paid social media campaigns. If you want to do UX design for an eCommerce startup, you can publish a series of UX critiques about popular eCommerce sites on a blog. Engineers rarely depend on resumes alone anymore; they demonstrate their experience by publishing their code to GitHub. 

Even opening an account on Medium.com and writing commentary on the industry you’re interested in can go a long way. Coupling this level of initiative with your previous (or nonexistent) work experience is the best way to demonstrate your talents and potential. In addition to gaining relevant skills that will assist you in a new role, you’ll appear to be both passionate about the subject matter and a knowledgeable self-starter who practices it in your spare time.

4. Do your homework. Then, do some more.

With a solid network, clear thesis, and foundation of experience, it’s only a matter of time before you start landing interviews. Most recruiters will tell you at this point to spend 12 hours preparing for an interview. We think that’s child’s play. You aren’t interviewing to be a cog in a massive corporate machine. You are being assessed on whether the founder or manager would bet the future of their budding company on you. Make them comfortable — and confident in you — by being the most prepared person in the room.

Find founders on Twitter, LinkedIn, or in the blogosphere and consume every bit of content you can find. The information you’ll find there is priceless because you will gain a deep understanding of how founders think and feel about the world. You can even head to Facebook and see if you have any mutual friends. Does all of this seem a little overboard? Perhaps, but startups expect a different level of commitment than many traditional careers. So if this sounds like a lot, you’ll be in for a big surprise once the job begins.

5. Play the numbers game. Ask metrics-driven questions.

In an interview with a startup, you really have three goal goals: 1) Clearly communicate why you’re capable of doing the job, 2) be the most passionate person in the room, and 3) ask the best questions. You certainly should ask standard interview questions, like “What makes someone successful in this role?” or “What will the first 90 days look like?” But what you really want to do in the interview is discover the metrics the company cares most about.

Sure, a company’s public brand may be all about changing the world, but we can guarantee that every night before they go to bed and every morning after they wake up, the person interviewing you is checking a dashboard with a handful of key metrics, such as cost to acquire a customer, lifetime value of a customer, net promoter score, or churn. When they leave your interview, they’ll probably check it again.

Metrics dictate performance, and in the uncertain conditions in which startups live, having insight into how well the business is doing is essential for a small team that has a lot of impact.

When you go into your interview, don’t be afraid to ask:

  • What metrics are you checking daily?
  • What metrics are you checking weekly?
  • What metrics are you checking monthly?
  • What do you see as the biggest levers for improving those metrics?
  • How are you doing against your goals?
  • How can this role help you get there faster?

The answer to those questions will give you everything you need to know to position yourself as the best fit for the job. For example, if you’re applying for a marketing job and learn in the interview that high product churn is keeping the founder up at night, you can follow up with an email with three ideas on how the company can immediately improve retention.

6. Pay attention to startup funding cycles.

Fundraising impacts everything about a startup, and understanding it can also serve as a huge advantage for you in your job hunt. When you read that a startup raised $15 million, it’s safe to assume it isn’t looking for a safe, high-yielding savings account to put it in. The company is going to put almost every cent to work by increasing marketing, improving the product, and, most importantly building the team it needs to take the business to the next level. There is literally no time when the ground is more fertile for you to land a job than immediately after a startup raises money. So it’s on you to stay on top of the news.

TechCrunch is an excellent resource for keeping up with fundraising news. The site will report on just about every dollar raised in the startup world. If you’re interested in a particular company, set up Google Alerts so you can be the first to know whenever a new round of funding comes in. If you want to be ahead of the curve, AngelList has a directory of all startups looking to raise their first round of funding. It’s also an excellent job board.

These tips are just a start — for more expert insight, download our free guide, How to Get a Job at a Startup. Discover firsthand tips on how to break into a startup career, clear up confusing industry jargon, and learn about important resources that will aid you on your journey. Good luck!

Essential Data Skills to Know

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In 2012, IBM revealed that 2.5 quintillion bytes of data were being created per day — an enormous sum that humankind had never known before. Since then, the volume of the world’s data has not only continued to increase, but it’s arriving at a faster and faster pace.

However, data by itself doesn’t have much value. After all, a pile of numbers and data files is just that: a pile of numbers and data files. The real value of data comes from making sense of the abundance of information. That’s why businesses and organizations across countless industries are investing in forward-thinking data talent — to leverage its predictive power, craft smart business strategies, and drive informed decision-making.

The sharp and strategic people who do this job are data scientistsdata analystsmachine learning engineers, and business intelligence analysts — among other titles — and these professionals are in high demand. In 2018, the jobs platform Glassdoor ranked data scientist as the Best Job in America for the third year in a row, with a median salary of $110,000 and more than 4,500 available positions. Additionally, five other data- and analytics-related roles made the list of the top 50 jobs, ranked by number of openings in the field, salary, and overall job satisfaction.

Companies are quickly recognizing the vital need for data knowledge, impacting a vast array of industries including eCommerce, health care, finance, and sales — to name a few. In order to stay competitive and grow their businesses, leaders are investing in their future by strategically training and hiring talent to ensure proficiency in key skills.

Three of the most prevalent technologies transforming how we understand and use data are SQL, Python, and machine learning — and all are great entry points into the field. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data scientists and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.

What You Can Do With Essential Data Skills

You can get started with SQLPython, and machine learning, three of the most useful data tools, without any formal background. However, each topic has a different set of fundamentals that you’ll need to understand as you progress in your learning. For example, Python will expose you to the world of object-oriented programming, while SQL will expose you to database design concepts. Machine learning will require a good understanding of data analysis.

Dipping your toes in this uncharted water may seem daunting — but it shouldn’t! There’s so much opportunity in the data field for growth, whether or not you’re seeking a full-time role. No matter your position or industry, this knowledge can take your hireability to the next level. Here are just some of the things you can do with data expertise:

  • Become a skilled problem-solver. Programming languages like SQL and Python teach you problem-solving skills that are applicable in many business scenarios you’ll encounter.
  • Be more cross-functional. Having key programming and data skills under your belt makes it easier to work with teams across your organization. Being able to speak the same language as software engineers, business intelligence analysts, and data professionals helps streamline requests, bring clarity to the workflow, and provide insight into technical action items.
  • Build the technology of the future. Data skills enable you to help build new, groundbreaking technologies, including web applications, machine learning models, chatbots, and much more.
  • Expand your career potential. Based on previous projections from the management consultancy firm McKinsey & Company, IBM predicts that by 2020, the number of data science and analytics job listings will grow by nearly 364,000 to about 2.72 million.
  • Improve communication. Data professionals must communicate to non-technical audiences — including stakeholders across the company — in a compelling way to highlight business impact and opportunity. At the end of the day, those stakeholders have to act on and possibly make far-reaching decisions based on data findings.

Want to learn more? In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three essential technologies. The skills go beyond data to bring delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown, and now’s a great time to dive in.

Download the paper to learn more.

Boost your business and career acumen with data.

Find out why machine learning, Python, and SQL are the top technologies to know.

Download the eBook

A Machine Learning Guide for Beginners

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Ever wonder how apps, websites, and machines seem to be able to predict the future? Like how Amazon knows what your next purchase may be, or how self-driving cars can safely navigate a complex traffic situation?

The answer lies in machine learning.

Machine learning is a branch of artificial intelligence (AI) that often leverages Python to build systems that can learn from and make decisions based on data. Instead of explicitly programming the machine to solve the problem, we show it how it was solved in the past and the machine learns the key steps that are required to do the same task on its own.

Machine learning is revolutionizing every industry by bringing greater value to companies’ years of saved data. Leveraging machine learning enables organizations to make more precise decisions instead of following intuition.

There’s an explosive amount of innovation around machine learning that’s being used within organizations, especially given that the technology is still in its early days. Many companies have invested heavily in building recommendation and personalization engines for their customers. But, machine learning is also being applied in a huge variety of back-office use cases as well, like to forecast sales, identify production bottlenecks, build efficient traffic routing systems, and more.

Machine learning algorithms fall into two categories: supervised and unsupervised learning.

Supervised Learning

Supervised learning tries to predict a future value by relying on training from past data. For instance, Netflix’s movie-recommendation engine is most likely supervised. It uses a user’s past movie ratings to train the model, then predicts what their rating would likely be for movies they haven’t seen and recommends the ones that score highly.

Supervised learning enjoys more commercial success than unsupervised learning. Some common use cases include fraud detection, image recognition, credit scoring, product recommendation, and malfunction prediction.

Unsupervised Learning

Unsupervised learning is about uncovering hidden structures within data sets. It’s helpful in identifying segments or groups, especially when there is no prior information available about them. These algorithms are commonly used in market segmentation. They enable marketers to identify target segments in order to maximize revenue, create anomaly detection systems to identify suspicious user behavior, and more.

For instance, Netflix may know how many customers it has, but wants to understand what kind of groupings they fall into in order to offer services targeted to them. The streaming service may have 50 or more different customer types, aka, segments, but its data team doesn’t know this yet. If the company knows that most of its customers are in the “families with children” segment, it can invest in building specific programs to meet those customer needs. But, without that information, Netflix’s data experts can’t create a supervised machine learning system.

So, they build an unsupervised machine learning algorithm instead, which identifies and extracts various customer segments within the data and allows them to identify groups such as “families with children” or “working professionals.”

How Python, SQL, and Machine Learning Work Together

To understand how SQLPython, and machine learning relate to one another, let’s think of them as a factory. As a concept, a factory can produce anything if it has the right tools. More often than not, the tools used in factories are pretty similar (e.g., hammers and screwdrivers).

What’s amazing is that there can be factories that use those same tools but produce completely different products (e.g., tables versus chairs). The difference between these factories is not the tools, but rather how the factory workers use their expertise to leverage these tools and produce a different result.

In this case, our goal would be to produce a machine learning model, and our tools would be SQL and Python. We can use SQL to extract data from a database and Python to shape the data and perform the analyses that ultimately produce a machine learning model. Your knowledge of machine learning will ultimately enable you to achieve your goal.

To round out the analogy, an app developer, with no understanding of machine learning, might choose to use SQL and Python to build a web app. Again, the tools are the same, but the practitioner uses their expertise to apply them in a different way.

Machine Learning at Work

A wide variety of roles can benefit from machine learning know-how. Here are just a few:

  • Data scientist or analyst: Data scientists or analysts use machine learning to answer specific business questions for key stakeholders. They might help their company’s user experience (UX) team determine which website features most heavily drive sales.
  • Machine learning engineer: A machine learning engineer is a software engineer specifically responsible for writing code that leverages machine learning models. For example, they might build a recommendation engine that suggests products to customers.
  • Research scientist: A machine learning research scientist develops new technologies like computer vision for self-driving cars or advancements in neural networks. Their findings enable data professionals to deliver new insights and capabilities.

Machine Learning in Everyday Life: Real-World Examples

While machine learning-powered innovations like voice-activated robots seem ultra-futuristic, the technology behind them is actually widely used today. Here are some great examples of how machine learning impacts your daily life:

  • Recommendation engines: Think about how Spotify makes music recommendations. The recommendation engine peeks at the songs and albums you’ve listened to in the past, as well as tracks listened to by users with similar tastes. It then starts to learn the factors that influence your music preferences and stores them in a database, recommending similar music that you haven’t listened to — all without writing any explicit rules!
  • Voice-recognition technology: We’ve seen the emergence of voice assistants like Amazon’s Alexa and Google’s Assistant. These interactive systems are based entirely on voice-recognition technology powered by machine learning models.
  • Risk mitigation and fraud prevention: Insurers and creditors use machine learning to make accurate predictions on fraudulent claims based on previous consumer behavior, rather than relying on traditional analysis or human judgement. They also can use these analyses to identify high-risk customers. Both of these analyses help companies process requests and claims more quickly and at a lower cost.
  • Photo identification via computer vision: Machine learning is common among photo-heavy services like Facebook and the home-improvement site Houzz. Each of these services use computer vision — an aspect of machine learning — to automatically tag objects in photos without human intervention. For Facebook, these tend to be faces, whereas Houzz seeks to identify individual objects and link to a place where users can purchase them.

Why You and Your Business Need to Understand Data Science

As the world becomes increasingly data-driven, learning to leverage key technologies like machine learning — along with the programming languages Python (which helps power machine learning algorithms) and SQL — will create endless possibilities for your career and your organization. There are many pathways into this growing field, as detailed by our Data Science Standards Board, and now’s a great time to dive in.

In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three data sectors. These skills go beyond data to bring delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown.

Individuals can use data know-how to improve their problem-solving skills, become more cross-functional, build innovative technology, and more. For companies, leveraging these technologies means smarter use of data. This can lead to greater efficiency, employees who are empowered to use data in innovative ways, and business decisions that drive revenue and success.

Download the paper to learn more.

Boost your business and career acumen with data.
Find out why machine learning, Python, and SQL are the top technologies to know.

Download the eBook

Python Programming for Beginners

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Python is the No. 1 most popular programming language used by data analystsdata scientists, and software engineers to automate processes, build the functionality of applications, and delve into machine learning. Companies like Google, SpaceX, and Instagram use it to clean data, build predictive and artificial intelligence (AI) models and web apps, and more. It stands out for being simple to read and write, while offering extreme flexibility and having an active community of users and contributors. This makes it a great language for new programmers to learn for a broad range of applications in data science, web development, and beyond.

Python in Everyday Life: Real-World Examples

Here are some fascinating ways in which Python is shaping the world we live in:

  • Artificial intelligence: Python is especially prevalent in the AI community, again for its ease of use and flexibility. For example, in just a few hours, a business could build a basic chatbot that answers some of the most common questions from its customers. To do this, programmers could use Python to scrape the contents of all of the email exchanges with the company’s customers, identify common themes in these exchanges with visualizations, and then build a predictive model that can be used by the chatbot application to give appropriate responses.
  • File-sharing applications: When the file-storage platform Dropbox was created in 2007, it used Python to build the desktop applications and server infrastructure responsible for actually sharing the files. After more than a decade, Python is still powering the company’s desktop applications. In other words, Dropbox was able to write a single application for both Macs and PCs that still works today!
  • Web applications: Python is used to run various parts of some of today’s most trafficked websites, including Pinterest, Instagram, Spotify, and YouTube. In fact, the visual bookmarking platform Pinterest has used Python in some form since it was founded (e.g., to power its web app, build and maintain data pipelines, and perform analyses).
  • Hollywood special effects: Remember that summer blockbuster with the huge explosions? A lot of companies, including Lucasfilm’s Industrial Light & Magic (ILM), use Python to help program those awesome special effects. By using Python, companies like ILM have been able to develop standard toolkits that they can reuse across productions, while still retaining the flexibility to build custom effects in less time than ever before.

Simplicity in Code

Here’s a cool example of just how simple Python is. Below is code that tells the computer to print the words “Hello World”:

In Python:

Python hello world

Yup, that’s really all it takes! For context, let’s compare that to another popular programming language, Java, which has a steeper learning curve (though is still a highly desirable skill set in the job market).

Java Hello World

Clearly, Python requires much less code. This powerful language’s ease of use makes it relevant far beyond data — coders have adopted it to perform all sorts of functions that you encounter every day.

Python at Work

A wide variety of roles can benefit from using Python. Here are just a few:

  • Data analyst: A data analyst could use Python to save time by automating tedious tasks or performing advanced calculations.
  • Data engineer: A data engineer could use Python to build a data pipeline that takes data from one system, aggregates it or changes its shape, and moves it into another system.
  • Software engineer/web developer: A software engineer or web developer could quickly use Python to build the next great web app.

Why You and Your Business Need to Understand Data Science

As the world becomes increasingly data-driven, learning to leverage key technologies like Python, SQL, and machine learning will create endless possibilities for your career and your organization. Now is a great time to dive in.

These skills have surprising uses beyond data, bringing delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown.

Individuals can use data know-how to improve their problem-solving skills, become more cross-functional, build innovative technology, and more. For companies, leveraging these technologies means smarter use of data. This can lead to greater efficiency, employees who are empowered to use data in innovative ways, and business decisions that drive revenue and success.

In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down Python, SQL, and machine learning. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data science experts and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.

Download the paper to learn more.

Boost your business and career acumen with data.

Find out why Python, SQL, and machine learning are the top technologies to know.

Download the eBook

A Beginner’s Guide to Customer Focus

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Customer Focus

In the tech world, we’ve figured out how to measure user behavior down to a granular level via web analytics. From gauging interest through time spent on a page, to prioritizing information based on heat mapping, many companies are determining user preferences without directly interacting with individuals. But, relying on analytics alone without constant user involvement robs us of the main driver of success in product development: customer focus.

Customer focus is a tactic that dissects the analytics behind customer behavior. This involves considering their perspectives, understanding their needs and wants, and getting to the root cause of the issue that’s the focus of your product development efforts.

In many cases, the cause of an underperforming product or service isn’t obvious. A product manager or user experience (UX) researcher who works closely with their internal user experience team has the best chance of uncovering it. Along with product managers, UX designers ensure customer focus is maintained throughout the product development process, leveraging techniques like stakeholder interviews, customer journey mapping, and usability testing. (Learn more about these and other key strategies for gaining customer insights in our free white paper, Human-Focused Design.)

Let’s explore each of these in more detail.

What Are Stakeholder Interviews?

Interviewing both internal and external stakeholders is an integral part of product development. It allows you to hear your customers’ understanding of the problem you’re trying to solve — in their own words. It also provides you with an opportunity to ask for additional information on insights that may have been gathered with analytical tools.

As a product manager or UX designer, it’s crucial to understand your customers’ actual current behavior, not just what they say they will do. For instance, if you are developing an app to help people get to the gym more frequently and consistently, asking a customer, “How often do you plan to go to the gym this year?” isn’t as beneficial as asking, “How often did you attend the gym last year?” Customers cannot tell you what they will do with much sense of accuracy. They can only recall what they have done. Using past experiences to focus the product on future customer behavior is key.

When interviewing stakeholders, it’s important to ask open-ended questions (i.e., questions that won’t result in a simple “yes” or “no” answer). This will not only assist in identifying the root cause of the problem you’re addressing but will also keep the customer talking. For example, if you’re looking to determine the value of cardio classes and asked, “Would you say cardio classes are better workouts than weight lifting?” you may get a dead-end, one-word answer. When a customer just says “Yes,” what have you learned?

How do you get them to elaborate on the why in their response? It’s the why that separates a customer-focused product from one that’s solely data-driven. In this example, you could instead ask something like, “How do you feel about the cardio classes you participated in throughout 2017?”

How Customer Journey Mapping Works

Journey mapping — i.e., the strategic process of capturing and communicating complex customer interactions — provides you, the product manager, with a step-by-step visual representation of your customers’ current behavior surrounding the problem your product is trying to solve. When you’re able to walk through a process with a customer, you realize just how much is overlooked with conversation alone. Journey mapping can be done via sticky notes or by physically following customers through their daily activities. Both methods are effective in their own ways and commonly used in professional environments.

Journey mapping also helps connect a customer’s interview answers with their actual routines to provide deeper insights into their behaviors, wants, and needs. Oftentimes, people don’t realize how much they do until you ask them to walk you through their processes. With journey mapping, product managers and UX designers can also ask follow-up questions to support a stakeholder interview as necessary.

The Basics of Usability Testing

Conducted by both UX and product teams, usability testing observes customers’ interactions as they attempt to complete different tasks or transactions with a product and validates that product against a need, an idea, and an assumed solution. In other words, it allows them to confirm that the product effectively addresses a user problem they’ve aimed to solve. Through usability testing, users are able to navigate through a proposed solution and provide feedback on what they do and don’t like. Ideally, it should be conducted throughout the design process as the product evolves to meet customer needs. A customer may need to hold on to your product for a period of time in order for you to decide if it solves their problem effectively.

Customer-focused product development requires you to truly immerse yourself in the life of the customer. It pulls you out of what you think you know and places you in a position to learn. As you dig deeper into your customers’ behaviors, wants, and needs, you’ll strengthen the overall quality of your product.

Customer Focus at General Assembly

At General Assembly, product management and UX design students learn to hone customer-focused perspectives through hands-on practice. In our part-time Product Management course, you’ll bring a product idea to life via stakeholder interviewing, journey mappingusability testing, and more. Apply these techniques to optimize user-friendly products and services in our part-time User Experience Design courses, on campus or online. Or, take the first step toward a new career in our full-time User Experience Design Immersive.

Ask a Question About Our Business Programs

Meet Our Expert

Sherika Wynter is a jack of all trades. She works as senior product manager at USAC and has a background in mechanical engineering and industrial design. Prior to her current role, Sherika spent eight years as a project manager, working with clients including Pew Charitable Trusts, Tishman Speyer, and PBS. She holds certifications in both PMP and scrum master. Sherika currently teaches Product Management and project management workshops at General Assembly in Washington, D.C.

Sherika Wynter

“Product managers are integral to any product team. Product management is where design, development, and business intersect and, in this age of innovation, it’s skills are key to developing a successful product.”

Sherika Wynter, Product Management instructor, GA Washington, D.C.

SQL for Beginners

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Today we’re constantly bombarded with information about new apps, hot technologies, and the latest, greatest artificial intelligence system. While these technologies may serve very different purposes in our lives, many of them have one essential thing in common: They rely on data. More specifically, they use databases to capture, store, retrieve, and aggregate data.

This begs the question: How do we actually interact with databases to accomplish all of this? The answer: We use Structured Query Language, or SQL (pronounced “sequel” or “ess-que-el”).

Put simply, SQL is the language of data — it’s a programming language that allows us to efficiently create, alter, request, and aggregate data from databases. It gives us the ability to make connections between different pieces of information, even when we’re dealing with huge data sets.

Modern applications can use SQL to deliver valuable pieces of information that would otherwise be difficult for humans to keep track of independently. In fact, pretty much every app that stores any sort of information uses a database. This ubiquity means that developers use SQL to log, record, alter, and present data within the application, while analysts use SQL to interrogate that same data set in order to find deeper insights.

SQL at Work

A wide variety of roles can benefit from using SQL. Here are just a few:

  • Sales manager: A sales manager could use SQL to increase sales by comparing the performance of various lead-generation programs and doubling down on those that are working.
  • Marketing manager: A marketing manager responsible for understanding the efficacy of an ad campaign could use SQL to compare the increase in sales before and after running the ad.
  • Business manager: A business manager could leverage SQL to streamline processes by comparing the resources used by various departments in order to determine which are operating efficiently.

SQL in Everyday Life: Real-World Examples

We’re constantly interacting with data in our lives, which means that, behind the scenes, SQL is probably helping to deliver that information to us. Here are a few examples:

Extracting Data

At its most basic, SQL is about accessing data locked away in databases. Think about the last time you received a report about how your company or team is performing. This probably had some key metrics like sales figures, conversion rates, or profit margins based on data stored in a system like a customer relationship management (CRM) or eCommerce platform.

A developer or analyst, or maybe even you, used SQL in order to access the data needed to produce that report.

Web Applications

Think about the last time you looked up the name of a movie on IMDb, the Internet Movie Database. Perhaps you quickly noticed an actress in the cast list and thought something like, “I didn’t realize she was in that,” then clicked a link to read her bio.

As you were navigating through that site, SQL may have been responsible for returning the information you “requested” each time you clicked a link.

Synthesizing Data to Make Business Decisions

With SQL, you can combine and synthesize data from different sources, then use it to influence business choices.

For example, if you work at a real estate investment firm and are trying to find the next up-and-coming neighborhood, you could use SQL to combine city permit, business, and census data to identify areas that are undergoing a lot of construction, have high populations, and contain a relatively low number of businesses. This might present a great opportunity to purchase property in a soon-to-be thriving neighborhood!

Why You and Your Business Need to Understand Data Science

On a high level, data professionals collect, process, clean up, and verify the integrity of data. They apply engineering, modeling, and statistical skills to build end-to-end machine learning systems that uncover the ability to predict consumer behavior, identify customer segments, and much more. They constantly monitor the performance of those systems and make improvements wherever possible.

Looking at the field as a whole, there’s a wide array of tools available to help data experts perform tasks ranging from gathering their own data to transforming it into something that’s usable for their needs.

In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three prevalent technologies that are transforming how we understand and use data. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data scientists and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.

These skills have surprising uses beyond data, bringing delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown. Download the paper to learn more.

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