Data Science for Driving Growth in E-Commerce - Applying Domain Expertise to Solve Real-World Problems Using Data Science - Data Science For Dummies (2016)

Data Science For Dummies (2016)

Part 5

Applying Domain Expertise to Solve Real-World Problems Using Data Science

Chapter 20

Data Science for Driving Growth in E-Commerce

IN THIS CHAPTER

check Making sense of e-commerce data

check Applying analytics to optimize an e-commerce business

check Deploying web analytics to drive growth

check Testing, testing, and more testing

check Segmenting and targeting your audiences

Big data and analytics aren’t really new topics to most people these days. However, the creative ways in which big data and analytics are being used to transform lives and businesses are new. Businesses are quickly catching on to the fact that in this fast-paced era, an organization’s survival hinges on its ability to integrate data science and analytics into every major decision that’s made — particularly in relation to strategic marketing decision making. In fact, the demand for marketing analytics practitioners has increased by 136 percent in the past three years alone. Marketing analytics professionals use data science and analytics to drive sales growth and user adoption rates for today’s e-commerce business — a business that sells products or services on the Internet.

These days, even the most traditional, old-fashioned business enterprise has at least some sort of web presence that would qualify as an e-commerce operation. Other e-commerce businesses are 100 percent digital and have no real on-the-ground presence to speak of. Because many businesses use blogging to build a well-branded online space where visitors receive access to insightful or entertaining content in exchange for their website visits and brand loyalty, even an individual blogger who has a website and a strong social presence can be considered an e-commerce business.

In recent years, the practice of using marketing analytics and data science to develop tactical strategies for e-commerce business growth came to be known as growth hacking — growth hacking is also referred to as growth engineering, or simply growth. Growth hacking is particularly well suited for start-up growth because of the lower-cost, more-innovative methods that growth hackers generally employ. In marketing, the word conversiondescribes the scenario in which a marketing effort is successful in getting a user, or prospective user, to take a desired action. Examples of conversions include the act of getting someone to visit your website, subscribe to your newsletter, follow your social media channel, or purchase your product.

In the growth game, the only goals are to get visitors to convert and to move them along in a swift and steady flow through all layers of the sales funnel. This chapter provides you with some simple concepts and methods you can use to get started in growing your e-commerce business. This chapter gives you only the tip of the growth iceberg. For brevity, I’ve omitted many of the more advanced and complicated tactics.

True growth hacking is a hybridization of the following fields:

· Engineering: In an e-commerce context, this includes systems design, process design, systems thinking, and iterative design.

· Marketing: Subcategories of marketing include psychology, branding, and aesthetic design.

· Business intelligence: Think intelligence as in Central Intelligence Agency, rather than smarts. Subcategories here include metric selection, descriptive analytics, diagnostic analytics, and prescriptive analytics based on simple inference.

· Data science: Casting its web rather widely, data science requires math and statistics know-how, web programming chops, the ability to code in Python or R, SQL skills, and subject-matter expertise in e-commerce business and Internet marketing.

In this chapter, I discuss the data science that’s involved in growth hacking, and how you can use it to supercharge your online business growth. Just keep in mind that marketing analytics professionals who engage in data science for e-commerce growth may have to wear many hats — for example, Digital Analytics Consultant, Web Analytics Engagement Analyst, Digital Web Marketing Analytics, or Optimization Manager. For simplicity’s sake, I refer to all of these roles as e-commerce data science.

Here’s a look at the data science that’s involved in this line of work:

· Math and statistics requirements: Practitioners should understand and know how to apply significance testing, time series analysis, trend and seasonal analysis, regression analysis, multivariate regression, segmentation analysis, A/B split testing, and multivariate testing.

· Programming requirements: Data scientists working in growth should be solid in SQL, as well as web-programming languages like JavaScript, HTML, DHTML, and AJAX. Python and R programming could come in handy for segmentation analysis, data visualization, or building a recommendation engine, although not many growth specialists are required to do this type of work, because of the high availability of applications designed specifically for these purposes.

· Subject-matter expertise: Data scientists working in this field must have a deep understanding of e-commerce business, its various structures, its systems, and its channels. They must also understand Internet marketing fundamentals.

Data scientists in e-commerce generally use applications for their analyses, although sometimes they need to use coding to carry out a customized analysis. E-commerce data scientists use data science to formulate highly focused, results-oriented business strategies. They do not focus on exploratory data analysis. In e-commerce data science, your job is to use data in order to better understand users so that you can devise ways to drive growth results. You use algorithms and data visualizations only to achieve these goals. It’s all about how you as a data scientist can derive insights from a wide variety of software and web applications — many of which I discuss in the section “Appraising popular web analytics applications,” later in this chapter, by the way.

Data scientists in this field are often asked to analyze click-stream data, site performance data, and channel performance data in order to provide decision support on the effectiveness of tactical optimization strategies. They often have to design, develop, and manage tag deployments — the placing of code snippets in the header of a web page used to collect data for use in third-party analytics applications. Data scientists in this field also work on A/B split testing, multivariate testing, and mouse-click heat map analytics (all explained later in the section “Checking out common types of testing in growth”).

Advanced data scientists in this field may also be expected to build personalization and recommendation engines. Practitioners need to communicate data insights in a clear, direct, and meaningful manner, using written words, spoken words, and data visualizations. Lastly, any data scientist who works in growth has to have a solid understanding of e-commerce and Internet marketing.

Making Sense of Data for E-Commerce Growth

Data science in e-commerce serves the same purpose that it does in any other discipline — to derive valuable insights from raw data. In e-commerce, you’re looking for data insights that you can use to optimize a brand’s marketing return on investment (ROI) and to drive growth in every layer of the sales funnel. How you end up doing that is up to you, but the work of most data scientists in e-commerce involves the following:

· Data analysis: Simple statistical and mathematical inference. Segmentation analysis gets rather complicated when trying to make sense of e-commerce data. You also use a lot of trend analysis, outlier analysis, and regression analysis.

· Data wrangling: Data wrangling involves using processes and procedures to clean and convert data from one format and structure to another so that the data is accurate and in the format that analytics tools and scripts require for consumption. In growth work, source data is usually captured and generated by analytics applications. Most of the time, you can derive insight within the application, but sometimes you need to export the data so that you can create data mashups, perform custom analyses, and create custom visualizations that aren’t available in your out-of-the-box solutions. These situations could demand that you use a fair bit of data wrangling to get what you need from the source datasets.

· Data visualization design: Data graphics in e-commerce are usually quite simple. Expect to use a lot of line charts, bar charts, scatter charts, and map-based data visualizations. Data visualizations should be simple and to the point, but the analyses required to derive meaningful insights may take some time.

· Communication: After you make sense of the data, you have to communicate its meaning in clear, direct, and concise ways that decision makers can easily understand. E-commerce data scientists need to be excellent at communicating data insights via data visualizations, a written narrative, and conversation.

· Custom development work: In some cases, you may need to design custom scripts for automated custom data analysis and visualization. In other cases, you may have to go so far as to design a personalization and recommendation system, but because you can find a ton of prebuilt applications available for these purposes, the typical e-commerce data scientist position description doesn’t include this requirement.

Optimizing E-Commerce Business Systems

Time for a (brief) primer on how you can begin using web analytics, testing tactics, and segmentation and targeting initiatives to ignite growth in all layers of your e-commerce sales funnel. Before getting into the nitty-gritty of these methods, though, you first need to understand the fundamental structure and function of each layer in a sales funnel. In keeping with a logical and systematic approach, I’m breaking down the e-commerce sales funnel into the following five stages: acquisition, activation, retention, referral, and revenue.

remember Acquisition, activation, retention, referral, and revenue are also referred to as AARRR, the pirate metrics. (Say “AARRR” out loud a few times and you’ll know why it’s called the pirate metrics.) This growth framework (see Figure 20-1) was originally suggested by famed angel investor and entrepreneur, Dave McClure. The term is now widely used throughout the growth-hacking community.

image

FIGURE 20-1: The AARRR of the e-commerce sales funnel.

Here are the functions of each stage of the sales funnel:

· Acquisition: Your brand acquires new users in the form of website visitors. New users are often acquired via social media marketing, search engine marketing, search engine optimization, content marketing, or partnerships.

· Activation: Acquired users activate, either through email subscription, RSS subscription, or social followings.

· Retention: Activated users take some sort of action — such as accepting an offer or responding to a call to action within your email marketing campaign.

· Referral: Retained users refer new users to your brand’s acquisition layer.

· Revenue: Users make revenue-generating purchases.

Angling in on analytics

Web analytics can be described as the practice of generating, collecting, and making sense of Internet data in order to optimize web design and strategy. Configure web analytics applications to monitor and track absolutely all your growth tactics and strategies, because without this information, you’re operating in the dark — and nothing grows in the dark.

Web analytics provide fast and clear results that gauge e-commerce growth strategy effectiveness. You can use web analytics as a diagnostic tool, to get to know your audience, to understand their preferences, to start doing more of what works, and to stop doing the things that clearly don’t work. If you want to devise growth strategies that actually grow your business, you need to make sure you’ve configured web analytics to track and monitor all stages of the funnel, as well as every touch point between your brand and its prospective customers.

Appraising popular web analytics applications

Data scientists working in growth hacking should be familiar with (and know how to derive insights from) the following web analytics applications:

· Google Analytics (www.google.com/analytics): A free, easy-to-use, powerful web analytics tool, Google Analytics is great for monitoring not only the volumes of traffic that come to your website over time but also the demographics and summary statistics on your visitors, your website referral sources, your visitor flow patterns, real-time visitor behavior analytics, and much more. Google Analytics can show you benchmarking analytics that provide insights about how your website’s performance compares to the performance of other websites in your industry.

· Adobe Analytics (www.adobe.com/solutions/digital-analytics/marketing-reports-analytics.html): You can use Adobe Analytics for marketing attribution, mobile app performance, social media marketing performance, return-on-investment (ROI) investigation, and real-time visitor monitoring.

· IBM Digital Analytics (www-03.ibm.com/software/products/en/digital-analytics): The perfect platform for integrating performance data from all your business’s web channels — from data generated by website guests visiting using personal computers to mobile visitor statistics, and even social media channel performance — IBM Digital Analytics offers powerful analytics capabilities to keep you informed of real-time and historical visitor behaviors, as well as relevant cross-channel interactions. The platform also offers marketing attribution and tag management capabilities.

· Webtrends (http://webtrends.com): Offering advanced multichannel analytics, real-time visitor behavior monitoring, and the technology you need to reclaim lost sales from shopping cart abandonment via email remarketing tactics, Webtrends is a powerhouse web analytics application. It even goes the extra mile by offering a campaign optimization feature that you can use to track, monitor, and optimize your search engine marketing efforts, as well as your search and social advertisement campaigns.

· Google Tag Manager (www.google.com/tagmanager): Website tags — code snippets that collect data for use in your third-party analytics applications — can help you measure and manage the effectiveness of your Internet marketing campaigns, but the process of deploying tags is error-prone and requires coding. Google Tag Manager is a free tag-management tool that offers a code-free interface and a rules-based system that allows you to easily manage and deploy your website marketing and tracking tags.

· Assorted social analytics tools: In addition to the more heavyweight offerings described in this list, you can find many free, easy-to-use social analytics applications to monitor and measure the effectiveness of your social media growth initiatives. These include Sendible (www.sendible.com), which has ample options for tracking statistics from your Twitter, Facebook Page, Instagram, and Google Analytics metrics on one custom dashboard; Facebook Page Insights (www.facebook.com/Your_Facebook_Page_ID/insights); Pinterest Analytics (https://analytics.pinterest.com); Iconosquare Statistics for Instagram (http://iconosquare.com); and Google URL Shortener for link tracking (https://goo.gl).

While a cookie is not a web analytic application, per se, it is a text file that tracks the activities, interests, and browsing patterns of a website’s visitors. Almost all large-scale e-commerce businesses use cookies to collect visitor information that helps the business improve the overall user experience and optimize advertising efforts.

Accessing analytics for acquisitions

Analytics for acquisitions provide a measure and gauge of the effectiveness of your user acquisition tactics. If you want to optimize your brand’s channels, to glean a deeper understanding of your audiences, or to evaluate the performance of your growth tactics, look to user acquisition analytics. In this list, I describe some means by which you can use web analytics to begin boosting your user acquisitions:

· Audience discovery: By taking a close look at your web analytics and the sources from which your new users are being acquired, you can infer an idea about the interests of users in each of your channels.

· Channel optimization: After discovering insights about your channel audiences, you can use those insights to optimize your channels — designing your channels and the offerings you extend along them so that they better align with the preferences of each channel audience.

· Optimized social-growth strategies: Social media networks are brand channels. Each network functions for its own purpose, and the preferences of audience members in different networks tend to differ, even if the niche is the same. For example, content about news events tends to perform well on Twitter, whereas Facebook audiences seek to be entertained and inspired. News content doesn’t fare so well on the Facebook network and vice versa. What’s more, your specific audiences have their own, particular interests and nuances per social network. Use social analytics to deduce the interests of your audiences per social channel, and then you can use that information to optimize your efforts there. You can also use social network analytics to identify the main influencers in your niche so that you can begin forging friendships and strategic alliances.

Applying analytics for activation

User activation analytics provide a measure and gauge of your user activations over time. You can use activation analytics to gauge how your user-activation tactics are performing, allowing you to optimize your user sign-ups, even on a per-channel basis. The following are a few ways in which you can use web analytics to optimize your user activation growth rates:

· Sign-up rate monitoring: Analytics that reflect the number of new user sign-ups, in the form of either email subscriptions or RSS subscriptions. This metric gives you an idea of how well your website’s content is meeting the wants and needs of newly acquired users. These analytics are also a good gauge of the overall effectiveness of your calls to action — your prompts that tell users to sign up in exchange for some promised benefit to them.

· Average session duration: You can easily derive information on average session duration by taking a quick and basic look at your Google Analytics. Average session duration is a good gauge of how compelling your visitors find your website. And the more compelling your site, the more likely it is that your acquired users will convert to active users — and active users to refer their friends and convert to paying customers.

tip If you’re working on growth for a client or employer, you can access their Google Analytics account by having them add your Google account as an authorized user of their Google Analytics account. If you’re working on growth for your own brand or website, you must sign up for a free Google Analytics account (at www.google.com/analytics) and then install the Google Analytics Tracking code into your site.

remember Whether you’re working on behalf of a client or yourself, you must have your own Google account. You can get one of those by registering through Google (at https://accounts.google.com/SignUp).

· Website heat maps for website optimization: A website heat map is a visual graphic that uses colors to depict the areas of a web page where visitors are clicking with greatest and least intensity. Applications such as SessionCam (www.sessioncam.com/website-heatmaps) and ClickTale (www.clicktale.com/products/heatmap-suite) offer mouse-click heat map data visualizations that show you how your customers and user segments are using your website — in other words, what website features and areas are most attractive to users. This information tells you about the effectiveness of your activation tactics and your overall web design. If you see that user attention flow isn’t focused toward your call-to-action areas, you should perhaps redesign your page in a way that helps to redirect user focus.

remember Your main goal should always be to push users toward the next stage of the sales funnel.

Reviewing analytics for retentions

Retention analytics provide a measure of your user retention tactics. Retention analytics can help you boost customer loyalty or increase the amount of time your users spend interacting with your brand. Boosting user retentions is, in large part, a function of marketing strategy and psychology, but web analytics are also an integral part of maintaining and growing your brand’s retention rates. Here’s how you can use web analytics to optimize your user retentions growth:

· Email marketing open rates: Tracking and monitoring time series — collections of data on attribute values over time — that capture email open rates can give you an idea of how well your email marketing tactics are performing, in general. For example, if you see a steady decline in open rates, either your subscribers aren’t that interested in the topics described in email headlines or you’re sending emails too frequently and spamming your users’ inboxes — in other words, wearing out your welcome. High email-open rates reflect a high level of subscriber loyalty, which is always a good thing.

· RSS view rates: Tracking and monitoring time series that capture RSS view rates can give you an idea of how well your blog post titles are performing with your RSS subscribers — in other words, how well the blog content topic is matched to your subscribers’ interests. This metric can also tell you whether your headline copy is intriguing enough to draw RSS subscribers in for a read. High RSS view rates reflect higher levels of loyalty among your RSS subscribers.

· Customer satisfaction monitoring: Sentiment analysis is an analysis technique where you apply text mining and data categorization techniques to web data in order to identify the feelings and attitudes of people (and customers) in your networks. Some social analytics applications offer a built-in sentiment analysis feature. You can use one of these applications or code something up yourself. Whatever you choose, be sure to stay on top of what people are saying about your brand across your social media channels, because it’s vital for the protection and proactive management of your brand’s reputation. As they say, “The customer is always right.”

Talking about testing your strategies

In growth, you use testing methods to optimize your web design and messaging so that it performs at its absolute best with the audiences to which it’s targeted. Although testing and web analytics methods are both intended to optimize performance, testing goes one layer deeper than web analytics. You use web analytics to get a general idea about the interests of your channel audiences and how well your marketing efforts are paying off over time. After you have this information, you can then go in deeper to test variations on live visitors in order to gain empirical evidence about what designs and messaging your visitors actually prefer.

Testing tactics can help you optimize your website design or brand messaging for increased conversions in all layers of the funnel. Testing is also useful when optimizing your landing pages for user activations and revenue conversions. In the following sections, I introduce the testing strategies that are most commonly deployed in growth and discuss how you can use those strategies to optimize your efforts. I also provide you with a few tips on what applications are available to make testing easier and more fun.

Checking out common types of testing in growth

When you use data insights to increase growth for e-commerce businesses, you’re likely to run into the three following testing tactics: A/B split testing, multivariate testing, and mouse-click heat map analytics.

An A/B split test is an optimization tactic you can use to split variations of your website or brand messaging between sets of live audiences in order to gauge responses and decide which of the two variations performs best. A/B split testing is the simplest testing method you can use for website or messaging optimization.

Multivariate testing is, in many ways, similar to the multivariate regression analysis that I discuss in Chapter 5. Like multivariate regression analysis, multivariate testing allows you to uncover relationships, correlations, and causations between variables and outcomes. In the case of multivariate testing, you’re testing several conversion factors simultaneously over an extended period in order to uncover which factors are responsible for increased conversions. Multivariate testing is more complicated than A/B split testing, but it usually provides quicker and more powerful results.

Lastly, you can use mouse-click heat map analytics to see how visitors are responding to your design and messaging choices. In this type of testing, you use the mouse-click heat map to help you make optimal website design and messaging choices to ensure that you’re doing everything you can to keep your visitors focused and converting.

remember Landing pages are meant to offer visitors little to no options, except to convert or to exit the page. Because a visitor has so few options on what he can do on a landing page, you don’t really need to use multivariate testing or website mouse-click heat maps. Simple A/B split tests suffice.

Data scientists working in growth hacking should be familiar with (and know how to derive insight from) the following testing applications:

· Webtrends (http://webtrends.com): Offers a conversion-optimization feature that includes functionality for A/B split testing and multivariate testing.

· Optimizely (www.optimizely.com): A popular product among the growth-hacking community. You can use Optimizely for multipage funnel testing, A/B split testing, and multivariate testing, among other things.

· Visual Website Optimizer (https://vwo.com): An excellent tool for A/B split testing and multivariate testing.

Testing for acquisitions

Acquisitions testing provides feedback on how well your content performs with prospective users in your assorted channels. You can use acquisitions testing to help compare your message’s performance in each channel, helping you optimize your messaging on a per-channel basis. If you want to optimize the performance of your brand’s published images, you can use acquisition testing to compare image performance across your channels as well. Lastly, if you want to increase your acquisitions through increases in user referrals, use testing to help optimize your referrals messaging for the referrals channels. Acquisition testing can help you begin to understand the specific preferences of prospective users on a channel-by-channel basis. You can use A/B split testing to improve your acquisitions in the following ways:

· Social messaging optimization: After you use social analytics to deduce the general interests and preferences of users in each of your social channels, you can then further optimize your brand messaging along those channels by using A/B split testing to compare your headlines and social media messaging within each channel.

· Brand image and messaging optimization: Compare and optimize the respective performances of images along each of your social channels.

· Optimized referral messaging: Test the effectiveness of your email messaging at converting new user referrals.

Testing for activations

Activation testing provides feedback on how well your website and its content perform in converting acquired users to active users. The results of activation testing can help you optimize your website and landing pages for maximum sign-ups and subscriptions. Here’s how you’d use testing methods to optimize user activation growth:

· Website conversion optimization: Make sure your website is optimized for user activation conversions. You can use A/B split testing, multivariate testing, or a mouse-click heat map data visualization to help you optimize your website design.

· Landing pages: If your landing page has a simple call to action that prompts guests to subscribe to your email list, you can use A/B split testing for simple design optimization of this page and the call-to-action messaging.

Testing for retentions

Retentions testing provides feedback on how well your blog post and email headlines are performing among your base of activated users. If you want to optimize your headlines so that active users want to continue active engagements with your brand, test the performance of your user-retention tactics. Here’s how you can use testing methods to optimize user retention growth:

· Headline optimization: Use A/B split testing to optimize the headlines of your blog posts and email marketing messages. Test different headline varieties within your different channels, and then use the varieties that perform the best. Email open rates and RSS view rates are ideal metrics to track the performance of each headline variation.

· Conversion rate optimization: Use A/B split testing on the messaging within your emails to decide which messaging variety more effectively gets your activated users to engage with your brand. The more effective your email messaging is at getting activated users to take a desired action, the greater your user retention rates.

Testing for revenue growth

Revenue testing gauges the performance of revenue-generating landing pages, e-commerce pages, and brand messaging. Revenue testing methods can help you optimize your landing and e-commerce pages for sales conversions. Here’s how you can use testing methods to optimize revenue growth:

· Website conversion optimization: You can use A/B split testing, multivariate testing, or a mouse-click heat map data visualization to help optimize your sales page and shopping cart design for revenue-generating conversions.

· Landing page optimization: If you have a landing page with a simple call to action that prompts guests to make a purchase, you can use A/B split testing for design optimization.

Segmenting and targeting for success

The purpose of segmenting your channels and audiences is so that you can exact-target your messaging and offerings for optimal conversions, according to the specific interests and preferences of each user segment. If your goal is to optimize your marketing return on investment by exact-targeting customized messages to entire swathes of your audience at one time, you can use segmentation analysis to group together audience members by shared attributes and then customize your messaging to those target audiences on a group-by-group basis. In the following sections, I tell you what applications can help you make user segmentation easier and how you can use segmentation and targeting tactics to grow the layers of your sales funnel.

Segmenting for faster and easier e-commerce growth

Data scientists working in growth hacking should be familiar with, and know how to derive insight from, the following user segmentation and targeting applications:

· Google Analytics Segment Builder: Google Analytics (www.google.com/analytics) contains a Segment Builder feature that makes it easier for you to set up filters when you configure your segments within the application. You can use the tool to segment users by demographic data, such as age, gender, referral source, and nationality. (For more on the Segment Builder, check out the Google Analytics Help page at https://support.google.com/analytics/answer/3124493.)

· Adobe Analytics (www.adobe.com/solutions/digital-analytics/marketing-reports-analytics.html): You can use Adobe Analytics for advanced user segmentation and customer churn analysis — or analysis to identify reasons for and preempt customer loss.

remember Customer churn describes the loss, or churn, of existing customers. Customer churn analysis is a set of analytical techniques that are designed to identify, monitor, and issue alerts on indicators that signify when customers are likely to churn. With the information that’s generated in customer churn analysis, businesses can take preemptive measures to retain at-risk customers.

· Webtrends (http://webtrends.com): Webtrends’ Visitor Segmentation and Scoring offers real-time customer segmentation features that help you isolate, target, and engage your highest-value visitors. The Conversion Optimization solution also offers advanced segmenting and targeting functionality that you can use to optimize your website, landing pages, and overall customer experience.

· Optimizely (www.optimizely.com): In addition to its testing functionality, you can use Optimizely for visitor segmentation, targeting, and geotargeting.

· IBM Product Recommendations (www-01.ibm.com/software/marketing-solutions/products-recommendation-solution): This solution utilizes IBM Digital Analytics, customer-segmentation, and product-segmentation methods to make optimal product recommendations to visitors of e-commerce websites. IBM Product Recommendations Solutions can help you upsell or cross-sell your offerings.

Segmenting and targeting for acquisitions

You can optimize your acquisition efforts to meet the exact preferences and interests of your prospective users. If you want to maximize your user-acquisition return on investment, you can use segmenting and targeting to group your prospective users and channels by interest and style preferences, and then use those groupings to send out exact-targeted messaging to prospective users en masse. Acquisitions segmentation and targeting helps you build your channels by providing solid facts about the preferences of particular segments. After you have prospective users grouped by preference, it’s just a matter of marketing to those preferences and avoiding messaging that’s unfavorable within the segments.

Prospective user and channel segmentation and targeting is the low-lying fruit of acquisitions growth because after you figure out what works with each segment, it’s just a matter of continuing to provide that content in order to make your user acquisition numbers grow. Here’s how you can use segmentation and targeting tactics to optimize your user acquisitions (which is the same goal as the tactics discussed in the section “Accessing analytics for acquisitions,” earlier in this chapter):

· Audience discovery: By performing segmentation analysis on your website visitor data, you can successfully group your website visitors in certain and distinctive classes according to their shared characteristics. This approach is far more definitive than the simple inference-based method used for analytics, but the purpose is the same — to use visitor data to better understand who your audiences are, what they’re interested in, and how you can best target your messaging and offerings to appeal to them.

· Social media channel optimization: You can use the insights you’ve gleaned via segmentation analysis to better understand and cater to the distinct preferences of your social media network audiences.

Targeting for activations

You can increase your user activations by understanding and responding to the interests and preferences of your website users. If you want to optimize your website and its content for increased user activations, segmentation analysis can help you get a better understanding of your audiences’ interests. Here’s how you can use segmentation and targeting tactics to optimize your user activation growth:

· Audience discovery: You can perform segmentation analysis of your website visitor data in order to understand and substantively group users according to their types and preferences. These groupings help you develop more strategically targeted messages to pique the interests of people in your audience segments.

· Strategic channel messaging: After you have a solid understanding of your user segments and their preferences, you can use this information to help you develop strategic, highly targeted messaging that performs well within each of the separate segments. This targeted approach can lead to increased social media followings and increased website subscriptions.

Segmenting and targeting for retentions

You can increase your user retentions by understanding and responding to the interests and preferences of your website users. To help increase user retention by reducing customer churn, you can deploy user segmentation and targeting strategies. Simply segment your customer-churn data into cohorts — subsets that are grouped according to similarities in some shared characteristic — and then analyze those cohorts to uncover trends and deduce the factors that contribute to churn within each of the groups. After you understand why particular segments of users are churning, you can take preemptive measures to stop that churn before you lose the customers for good.

Segmenting and targeting for revenues

You can increase your brand’s revenues by understanding and responding to the interests and preferences of your e-commerce customers. User segmentation and targeting strategies can help you increase revenues and sales volumes. Here’s how:

· Landing and e-commerce page optimization: You can use segmentation analysis on your website visitor data to better understand visitor behavior patterns per customer category, where a customer category could be defined by age, race, gender, income, referral source, or geographic region. After you distinguish clear user segments, and the preferences thereof, you can use that information to create separate, customized landing or e-commerce pages that are targeted for optimal sales conversions within the segments.

· Recommendation engines: Whether you build them yourself or use a recommender application instead, recommendation systems use collaborative filtering or content-based filtering to segment customers according to shared characteristics. It’s useful to segment customers in this way so that you can exact-target offers per customers’ known preferences, in order to upsell and cross-sell your brand’s offerings.