Customer Analytics Part IV: Journey Mapping

Now that you’ve learned where to look for customer data, thought about how to put that data in context, and ensured that your data is set up for comparison and visualization, let’s explore an actionable analysis of all that data—the customer journey map.

What is a Customer Journey Map?

A customer journey map plots the steps and touchpoints that lead a customer to using your product or service. It’s a visualization that shows the flow from the customer finding your product through the steps the customer takes to learn more, to purchase or to abandon their interaction.

The goal of a journey map is to unmask the hidden stumbling blocks that prevent an interested consumer from converting into a customer and to see what parts of their interaction run most smoothly. Looking at your company from the perspective of your customer gives you a larger picture of how your various departments are interacting and supporting your company’s goals.

Framing the Journey: How to Set Up Your Customer Journey Map

1.  Do you want to explore the whole journey or narrow your focus on a specific channel? You first need to determine if you are trying to get a broad picture, or if you want to understand, for example, how well your website is working.

⋅  Broad journey analysis—The big picture view of all of your customer touchpoints will start before the customer has ever interacted with you and will continue after she uses your product to consider referrals and repeat purchasing. This is a complex analysis with a large number of touchpoints, but it gives you the opportunity to understand how each part of your company is working toward your goals.

⋅  Specific journey—Narrowing your focus will let you dive more deeply into understanding your main channels of interaction, such as your website. In this case, you will gather detailed data on how a customer navigates the particular channel, when he needs to switch channels, or if the channel is often abandoned.

2.  How will you focus on a customer’s perspective? You next need to decide from which customer’s point of view you will map a customer journey. Then, build a persona that allows you to explore—maybe it is based on your highest value customers or you might focus on first time conversions.

3.  What information about the customer will you track?

⋅  Motivations—What is the customer looking for at a particular time in their journey? What are their goals? You want to make sure your company is engaging and giving a customer the tools she needs to meet her goal at each touchpoint.

⋅  Experience—What are the options for the customer at each touchpoint? Consider the tools you provide to a customer, and also any external options available, such as Google or competitor websites.

⋅  Itemize the links you have provided in emails, the search options on your website, the customer support options on- and offline; map out exactly what is available to your customer at each stage of interaction.

⋅  Customer thoughts and feelings—How happy is the customer with what your company has provided at each stage of her interaction?

Think about a customers’ general feelings: e.g. will they be feeling overwhelmed because they researching a complicated product? Then use your data to figure out how they feel about the service they are receiving from your company at each particular touchpoint: e.g., are you helping to alleviate the stress that makes their research overwhelming?

4.  Bring in the data—Once you’ve set up your framework and mapped the company’s services at each touchpoint (email ads and links, website tools, customer service on- and offline), look to your analytics.

Look at engagement rates and abandonment rates, and qualitative feedback including customer inquiries and survey responses, at each of the stages you identified to uncover those hidden problem areas that prevent customer satisfaction.

Journey mapping is not a simple analysis; but it is one of the best ways to combine knowledge from all of your customer data. Take a look at some example journey maps and begin planning to capture the right data to complete the picture of your customer’s journey.

Customer Analytics Part III: Using & Managing Your Data, A Checklist

Today we continue our Customer Analytics discussion with a quick checklist for your customer data analysis.

Three Steps to Customer Data Analysis

1.  Identify the Key Performance Indicators (KPIs) that you will track and make sure you are capturing the relevant data.

This step is a combination of the knowledge from Parts I and II of our Customer Analytics discussion. With the wide ranging types of data that can be captured and the many comparison options to put the data in context, it is important to identify the key indicators of health and growth for your company.

This means looking at your company’s goals to decide which data sets and trends are most important to track. There are so many ways to track customer engagement that you can end up spreading your resources thin by trying to gather everything. Instead, prioritize the most relevant data and make sure you have the tools to get what you want.

Avoid ending up with a mass of random numbers by having a clear plan and a manageable list of benchmarks.

2.  Integrate your customer data into a single comparison tool and convert data into comparable units.

The next step is putting all your data together. If you are using different analytics tools to keep track of email engagements, A/B tests or survey results, you need a centralized repository that can blend your data. This means thinking through software integrations; do you have CRM software that will integrate all your analytics and how will you add external data?

Centralized data also needs to be standardized for comparison. You will want a central analytics tool that will let you transform data into standard units, allowing you to gauge and compare customer engagement and satisfaction from purchases as well as customer service calls. Pulling real-time comparisons requires software that allows you to set a standardization plan.

3.  Create a dashboard that lets you visualize your analysis.

Once your system is set up to load and compare all your data, you need a plan for converting the raw numbers into an easy-to-read format.

⋅  Use charts & graphs in conjunction with in-depth reports. Set up real-time-updating trend visualization in your analytics software. Use charts and graphs that reflect your core KPIs (hopefully a manageable number) and promote instant comprehension of your data. Also make in-depth reports on the underlying data easy to generate, so that you can easily dive deeper when you see changes.

⋅  Set up alerts for important comparison points and benchmarks. Identifying the small number of benchmarks that require the most awareness additionally helps narrow your focus. Setting alerts based on these benchmarks will give you the best chance to respond rapidly.

Now that we have mapped the steps for analyzing data, we’ll look more closely at how to conduct some of the relevant comparisons. Come back to the blog as we continue to unpack customer data.

Customer Analytics Part II: Putting the Data in Context

Welcome back to our Customer Analytics discussion. We’ve gone over what customer analytics are and where data is sourced. Now let’s take a look at how we assess that data and find usable information.

Focusing on Segments of Data in Context

After cataloguing what feels like an unwieldy amount of information about who our customers are, when and where they find our products online, and how they navigate our webpages, it isn’t always obvious what to take away from the raw numbers. To make them meaningful, we need to think about the relationship between new customer data and other benchmarks, including:

1.  Industry data—Look for markers that are significant to your goals and compare your customer data with your competitors’.

⋅  Ask questions such as: Are you converting visitors to customers more or less often than others in the industry? How many visitors do competitor webpages get in comparison to yours? Do you drive visitors from the most common channels or are you serving a niche audience that is funneling from a particular source?

⋅  Use comparison tools—There are a host of tools that let you check in on competitor website traffic, search phrases that lead to your competitors, and online or social media chatter about your competition. Check out this list of comparison tools for some of your options.

⋅  Look at industry reports. For example, Wolfgang Digital’s E-commerce KPI Benchmarks 2017 Report is jam-packed with e-retailer benchmarks to compare against. An important one: e-commerce sites average a 1.6% conversion rate—how does your site compare?

2.  Time—Compare your new customer data to your old data.

⋅  Have customer interactions and purchases changed over time? Compare specific data sets over time, e.g. overall purchases or customer lifetime value. Choose a few markers of growth and chart each over time to get a wider view of your company’s overall change.

⋅  Are there are changes in relation to internal and/or external variables? Track changes that occur after shifts within the company, such as a particular ad campaign, but also after macro-level shifts, such as changes in industry regulations.

3.  Channel comparisons—Map sales and engagement data from various channels to compare how each performs.

⋅  Use a combination of raw numbers and percentages—I.e. look at both the quantity of purchases from a social media channel and the percentage of viewers who converted. Looking at both will you give you the most accurate understanding of the highest sales and the best ROI.

4.  Customer models—Map the steps that are most common to completing a purchase, and also identify the common demographics among your customers.

⋅  Combine your information on these two aspects of customer information to get a more complete picture of how, when and where to reach your customers. Use your data to get a stronger picture of your average customer and your highest value customers.

Still feel like you have a lot of questions about how to really unpack the value of all your customer information? There’s more to our Customer Analytics discussion, so come back to the blog as we continue to dive into Big Data.

Elements of E-Commerce | Customer Analytics

Welcome to Thanx Media’s “Elements of E-Commerce” blog series. Follow along as we wade through the nuts and bolts of e-commerce technologies that you need to know.

Today we’re taking a look at customer analytics. The first step is orienting ourselves with the what and where. Over the next few posts, we’ll dive deeper to start unpacking the bigness of Big Data.

What are Customer Analytics?

It’s not just having all the data, but using the data that we have on our customers. Customer analytics track customer demographics and the various touchpoints that connect a customer to a product or service. Instead of tracking general sales numbers following a marketing campaign, we should now track and analyze data on how specific customers interact with that campaign.

When done right, analytics offer insights that let us target products and offers to the most interested customers and show us what customers need to feel they received good service.

Where does the Data Come From?

Everywhere—there are more sources of data than there are people. Really. By 2008, the number of internet-enabled devices had outpaced the number of people in the world; and those devices keep multiplying. And in addition to the connection that the Internet brings, there are all the old connection points to keep track of as well.

So when beginning a plan for customer analytics, the first step is to identify all of the places that you can get information about your customers. Most B2B and B2C e-retailers can look to these:

1.  Website traffic, including:

⋅  Number and frequency of visitors.

⋅  Visitor geographic locations.

⋅  Time spent on the site by visitors.

⋅  Popular pages, and the most used or unused parts of a website.

⋅  Individual visitor navigation—the pages visited by particular visitors, frequent/repeated navigation tasks, length of visits.

⋅  Time spent browsing before completing a purchase.

⋅  Cart abandonment rates, in general and visitor-specific.

2.  Social media engagement, including:

⋅  Post-specific or campaign-specific views, engagements, and shares.

⋅  Click-throughs and conversion rates.

⋅  Follower acquisition and churn rate.

⋅  Chatter by other social media users referring to your company—positive and negative sentiments, referrals.

⋅  Social media influencers most followed by your customers, or by your target demographics.

3.  Internet noise—references to your company by other websites and blogs. Track:

⋅  Click-throughs and conversions.

⋅  Positive or negative sentiment.

⋅  Internet influencers most followed by your target demographic

4. Search results—internet keywords and typical searches that lead to your company.

5.  Email interaction, including

⋅  Contact signups, including for offers and newsletters.

⋅  Opened and engagement rates for emails.

⋅  Click-throughs and conversions.

6.  Other customer contacts, including:

⋅  Help/support calls, emails and chats.

⋅  Customer loyalty engagement, use, and service requests.

7. Offline advertising responses, including:

⋅  Sales following direct mail campaigns, sometimes tracked by campaign-specific offer codes.

⋅  Sales following TV/print ads.

8.  Customer surveys and feedback, on- and offline.

Obviously, there is almost endless information. With the right tools to sort and gather, and the right integration between your tools to track individual customer results, you’ll be able to narrow your focus into usable areas of this data. Come back next time and we’ll continue to unpack how to sort through it all.

Email Marketing Part II: Automation & Data Analysis

In the first part of our email-marketing discussion we outlined how to create personalized and interactive emails to engage consumers. But how do you achieve personalized subject headings for each reader or send emails in response to an individual consumer’s behavior? You need tech that automates these functions and gathers and analyzes data on customer demographics and behavior.

Let’s continue our email-marketing conversation by outlining the data-gathering and other technological features available to get your email read rates high.

Email-Marketing Software: Automation & Data-Tracking Features

⋅  Connect email to data about the customer’s journey — Gather a wide range of data, including your customer’s geographic location and time zone, browsing and search behavior, purchase history, and even the local weather. Using automation, integrate this data to determine when and what email offers should be sent.

⋅  Drip-email campaigns. Using a drip campaign, a company sends emails at various stages of a customer’s interaction with a product. They may offer additional education about the product, entice by highlighting certain features, act as reminders or make special offers. Each email responds to the last action the customer took, such as reading or failing to read a prior campaign email.

⋅  Triggered emails.  Set rules for follow-up emails to specific actions: e.g., automated reminder emails about items left in a shopping cart, or marketing emails about new products similar to past purchases.

⋅  Integrated CRM — Customer Relationship Management (CRM) software can integrate with various email-marketing platforms, allowing you to incorporate prior interactions when determining the content of targeted emails. Differentiate your potential clients from long-term customers and make emails responsive to individualized pricing and contracts.

⋅  Omni-channel integration — Tie email automation to multiple sources of customer behavior, including social media engagement and customer service chat history, for a more targeted follow-up to these interactions.

⋅  Segment email lists — Using demographic and customer behavior information, your complete email list can be divided into smaller groups for more targeted, individual-feeling email campaigns.

⋅  Email building — Email marketing software can make it easier to create email templates, optimize emails for mobile view, and even A/B test designs or messages.

⋅  Monitor campaigns — Collect data on interactions with your emails, including click-throughs rates and eventual purchasing or abandonment. Use this data to not only assess any campaign’s ROI, but to also assess the best tag lines, button sizes, interactive features and messages.

What features do you need? What will be most cost effective for your company? Get in touch, and Thanx Media can help you chose and implement the right email-marketing platform.

E-commerce Data Analytics: Do You Know What You’re Looking For?

Data analysis is becoming the center of successful and targeted e-commerce. Customers using websites, email and social media platforms give B2B and B2C e-retailers more information than was ever available for in-person sales. There is a way to use this data to strengthen focus on the strongest markets—for example, using predictive analysis tools Dell was recently able to send half the number of leads, but with a doubled return.

The question is, do you know what you are looking for? All of these online interactions mean almost endless behavior to track. How do you focus on the information that will help improve your marketing ROI?

Generating Long-Term Customers: Five Ways to Use Predictive Analytics

1.  New Customer ConversionWhat does a new shopper respond to that gets them to become a customer? Track inbound conversions from newsletters, emails and social media campaigns. Use data to find which products grab the most attention so that you know what should be front and center. Keep track of new sales that are tied to incentives, such as customer loyalty programs, to gauge what is most successful.

2.  Heat MapsWhat parts of your website are getting the most action? Heat map programs show you the hottest points on your site, so that you know what generates the most clicks and what items users are pretty much ignoring. Use this data to decide where information is placed on your site and where to add some graphic punch.

3.  Browsing Behavior Do your customers have a recurring focus? For example, your customers might consistently look at particular product specifications or shipping times most closely. Develop a sense of trust and increase your site’s ease-of-use by highlighting the information your customers find most relevant.

4.  Customer Long-Term ValueAre you generating repeat customers and which ones come back? If your marketing campaigns mostly get you single purchase customers, those campaigns may not be as successful as an initial sales figure suggests. Find common traits among repeat buyers to identify the strategies that create loyalty.

5.  Churn RateWhat spurs a customer to cut ties? Are you losing customers somewhere in the middle of their purchase decision? To successfully track your churn rate and understand what turns customers away, focus on both potential customers who abandon their carts and on previous customers who are no longer shopping—find the overlapping complaints and problems. Also, make sure it is easy for customers to share feedback so that you can consistently track and address complaints.