Solving Digital Conversion Struggles using Lifecycle Analytics

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Last time I wrote a post about the customer lifecycle my intention was to bring forth the idea of mapping customer’s journey in the digital purchase process. In this post, I would like to continue the conversation further building on the idea of the customer lifecycle analysis and how it can be made actionable. We will also discuss how to use customer experience analysis to drive improved conversion leading to higher revenue.

With an increase, in the number of marketing channels and devices (tablet, desktop or mobile) it is becoming harder to understand the customer behavior. Leading digital research agency, Econsultancy reports 91% of businesses failed to understand why visitors leave their digital properties without converting. This number makes me scratch my head in frustrations as every week a new digital or social tool is launched but businesses are still struggling to understand their customers.

The challenge is most of the tools in the market solve for only one specific problem causing marketers to run like chicken with no head from one vendor to the other looking for the holy grail. It’s time for us to go back to the fundamentals of analytics, which is “start with an end in mind” and set the right objectives. Quite honestly the startup craze is part responsible for the loss in focus by marketers. Although I am an avid fan of startups and have launched a couple of startups myself my intention is not to blame a fruitful industry. My goal is to shake some trees and drive focus on actionable analysis to solve business problems instead of chasing a shiny new tool.

So you may ask Sameer, then what is missing and why are organizations still struggling to understand their customers. IMHO the answer is not that simple, and we need to dissect the practice of analytics itself to get to the root cause of this issue. The best example I can share is from my experience leading several digital marketing and analytics organizations.

Let’s take the case of my previous employer where I was heading their digital, analytics and social teams. Our budget was pretty substantial, and we had crazy BHAG (Big Hair Audacious Goal). Not to mention the ever-changing direction of the business to keep up with the pace of the market and lastly, the proliferation of marketing channels and data. Then, there was product strategy, customer retention, sales enablement, lead nurture, customer experience, data integration and cross-functional marketing campaigns. My analytics team was a champion in performing what I call as nano analytics as most of their time was spent in driving insights to reach daily/weekly/monthly goals. Don’t get me wrong, we were very successful in what we did, and we have several case studies published showcasing our work. The problem was we had limited time to focus on macro analytics and to get deeper insight on our customers.

Long story short, we were finally able to convince our senior leaders to get additional funding and resources. We then used this for the launch of advanced analytics projects such as customer retention models, budget and sales forecast models that helped us significantly improve our overall business. We were lucky but most marketers are not able to get this type of additional funding to perform advanced analysis on their customer data.

Now let’s look at the other side of the issue. Typically, businesses heavily rely on web analytics for analyzing the visitor journey across their digital properties. In most cases, web analysis leads to identification of the standard metrics patterns through clickstream, pathing and funnel analysis. The quantitative data from web analytics does not provide insights into the customer’s journey across all digital properties or devices. Stitching together a complete picture of the user experience across all device types is also a major challenge for businesses.

Infusing social media and customer feedback data to digital analytics provides a additional context, although it can never give a full view of the customer experience. This clearly makes it difficult for businesses to get useful insights on revenue, sales, and customer experience over the long run.

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Source: Ipsos Public Affairs poll conducted for Ogilvy: IBM Tealeaf Survey 2014

One of the recent surveys (see above) conducted by Ogilvy on IBM Tealeaf customers revealed on average 50% of the customers left the website, tablet or mobile app after having a ‘poor’ or ‘very poor’ experience. This brings forth another issue, which is the siloed operation of customer experience and digital marketing organizations. UX organization’s key objective is to improve digital customer experience, and they excel at it. Digital marketers are focused on driving revenue and meeting sales goals. These business units are two sides of the same coin, and revenue cannot be driven without keeping customers happy whereas UX department funding comes from company revenue. It’s time for us rethink org structure and remove these unnecessary partitions to drive business growth.

Now let’s shift gear and discuss the solutions to these issues, so we can focus our attention the customer journey. The first and the foremost is the need for taking a holistic approach on analyzing digital data. We have to let go our personal bias and put the customers needs in the front and center. We still treat data as automobile and data analysis as an assembly line unit where one factory worker transfers the data to another worker for further torturing. Instead, integration of data set should be a prime focus which allows each business unit to look at the holistic data set and derive new insights.

As step 1, I am proposing combining the quantitative data (web analytics / digital analytics clickstream) with qualitative data (customer behavior and interaction, social opinions and feedback). There are different ways of integrating qualitative and quantitative data sets and there are different types of qualitative data. From my experience, the most effective form of integration is to focus on solving conversion issues and preventing customer abandonment. Notice I said, “customer abandonment” instead of cart abandonment because marketers tend to get lost in the technicalities of “cart abandonment” instead of focusing on the customers.

We should only focus on the customer’s journey and identifying the issues that have caused them to leave your digital properties. The power of focus can do magical things and as per quantum physics, our intentional focus on an object increases the objects vibration. It’s about time for marketers to learn every nuance about our customers at every step of the purchase cycle.

To illustrate the integration let’s walk through a fictitious conversion struggle scenario and how to use the right set of tools to solve conversion.

We are going to use a company called Planet Airlines. Planet Airline is a national airliner, and its value proposition is to deliver positive experience by putting customers in context. The airliner has a digital experience group headed by Janet Li who also leads their digital strategy.Picture1

Janet’s team has kicked started a new project called “customer 365” and the objective of this project is to:

  • Deliver positive one-to-one customer experience across all digital properties.
  • Improve customer loyalty by delivering hands-free customer support.
  • Increase installed base revenue.

To accomplish these goals Janet’s team has launched a new business-class upgrade campaign targeting Planet Airline’s existing customers. The mechanic of this campaign is as follows:

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Planet Airlines business-class upgrade campaign

When Planet’s customers log into their digital properties (mobile, tablet, web) to check-in, they receive a 20% discount offer message for business and comfort class upgrades. Customers who accept the offer are successfully upgraded to a business or comfort class. Campaign serves a dual purpose because customers are happy getting a good discount on business class and Planet makes more revenue out of existing customers.

To measure the performance of this campaign Janet creates milestone specific views of their customer’s journey. Each view or milestones represent an important phase of the customer’s upgrade cycle.

She creates three milestones:

Book flight upgrade: This milestone represents the customers who have accepted the 20% discount upgrade offer and have upgraded to business or comfort class.

Flight check-in: Customers who successfully complete check-in step are represented in this milestone.

Boarding pass: Finally, a milestone for customers who have downloaded their boarding passes.

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Customer Lifecycle view using IBM Tealeaf cxLifecycle Analytics

Janet’s team had originally set a benchmark conversion improvement of 20% from period A to period B for the upgrade campaign. She immediately notices the flight upgrade conversion reeling at 12.2%, which is a big gap from the 20% goal.

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Conversion issues with upgrade campaign

Next, she wants to understand the exact cause of the conversion issue, so she drills down the ‘Book Flight Upgrade’ milestone to identify the customer segment impacted by the conversion issue.

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Conversion issue isolated to mobile customers

She looks at the conversion by device types for customers using desktop, mobile and tablet devices and immediately notices a problem. The conversion for desktop and tablet users has improved, but the conversion for mobile users is down by 3%. Planet Airline owns two types of mobile digital property i.e. mobile app and mobile site. Janet wants to isolate the conversion issue to one segment, so she creates an ad-hoc report.

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Conversion issue isolated to mobile site customers

The ad-hoc report reveals few important pieces of information. First, Janet learns that the mobile app customer conversion is hovering at 4.8% average. Second, she finds out the conversion issue for business/comfort class upgrades is isolated to mobile site users.

After successfully identifying the customer segment impacted by conversion issue using quantitative analytics, she now wants to analyze qualitative behavior data. She selects the mobile site customer segment and exports the data to an already integrated session replay tool (here we will be using IBM Tealeaf). With a session replay tool, she can play the video replay of the individual abandoned sessions and learn about the customer’s behavior.

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Overview of abandoned sessions with option to replay each individual sessions

Within the replay tool, she can get a quick overview of successful and abandoned sessions. She selects one of the abandoned sessions and starts the replay.

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Session replay: Planet Airline mobile site logon screen

The first replay image shows the logon screen for the mobile site. Nothing important is displayed on the screen except for the fact the customer can see the logon screen without any issues. Janet continues the replay session and moves on to the second replay image.

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Session replay: Planet Airline mobile site cusotmer portal

The second replay image shows what happens when the customer enters their logon credentials and enters the customer portal. This image is showing the exact view of what this particular abandoned customer could see during their session. Janet immediately notices the revenue generating offer is hidden at the bottom of the screen whereas a non-revenue generating “welcome message” is showing up at the top. She now knows there is a problem with delivery of these messages, and email marketing team has given priority to the non-revenue generating message.

Befor she reaches out to the email marketing team, she wants to rule out technical issues. She quickly looks at the code level to analyze the mobile site code.

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Session replay: Code level analysis

Upon reviewing the mobile site code, she notices the issue lies within the JavaScript code. Planet Airline developers have inadvertently given higher priority to the welcome message and lower priority to the upgrade message. She emails the problem to the development lead to get it fixed.

As a next step, Janet wants to identify the total number of customers impacted by this issue, so she performs a quick search using the following criteria.

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Search for lost customers

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1,742 lost customers identified

Janet is now armed with the exact count and list of the lost customers. She quickly exports this 1,742 customer data back to the quantitative analytics tool because of the tight integrations for further visitor level analysis.

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Ad-hoc analysis and retargeting lost customers

In the ad-hoc reporting tool, she can run visitor level segmentation analysis to identify clickstream patterns such as marketing channels used, page access, and assets downloaded. She can also retarget these lost customers with an email or display ad to motivate them to return and complete the business-class upgrade.

Janet is successfully able to complete a full 360 analysis of its customers and is able to recover lost revenue. She is also able to map the customer journey using clickstream and behavior data. This type of analysis puts the marketer, UX person and the analyst in the drives seat and gives them the power to drive positive business results. It also overcomes the problem of siloed operations of marketing and UX teams by providing tight integrations between quantitative digital analytics, and qualitative user experience tools.

It’s your turn to provide your feedback, opinions and tweets!

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