Looking at key omni-channel analytics – Part 1

Over the past few weeks I’ve been learning about retailers and how they sell via a multitude of channels. The next thing for me now is to learn about some key omni-channel analytics. Let’s start with some questions to ask when measuring omni-channel retail and marketing:

What is the impact of online channels on offline and vice versa? – Given the fluid nature of consumer decision-making, alternating between online and offline, it’s important to measure the impact of online activities on offline and vice versa.
What does the conversion path look like? – How and where do we convert people into paying customers? Where do we lose people and why? Which channels do contribute to conversion and to which degree?

I’ll start by looking at the impact of online activities on offline conversion. I learned an awful lot from a 2008 blog post on tracking offline conversions by data guru Avinash Kaushik. Before I delve into some of Kaushik’s great suggestions, I want to take a step back and think about potential things to measure and why:

What is the impact of online channels on offline conversion? – As a product person, I’m keen to understand the relationship between online activities and actual purchases in-store. This understanding helps me to focus on the right online and offline elements of the value proposition, comprehending which things can be optimised inline to achieve a specific outcome in-store.
How do I best measure revenue impact of my website or mobile app in an omni-channel world? – For example, I’ve got a nice eCommerce site or app with a decent amount of traffic, 20% of which gets converted into actual online purchases. However, what happens with the remaining 80% of traffic that doesn’t get converted? Is my website or app delivering some value to this 80%!? If so, how? Can we measure this?

Now, let’s look at some practical tips by Kaushik in this respect:

Track online store locator or directions – If I track in an analytics tool the interactions with the URL for Marks & Spencer’s store locator, I can start learning about the number of Unique Visitors that are using the store locator in a certain time period (see Fig. 1 below). In addition, I can look at the number of visits or visitors where a certain post code or town has been entered into the store locator. I can take this insight as a starting point to learn more about the people within a certain geographical area that have a tendency to use the Marks & Spencer site and its store locator. Once a user then goes on to click on “Show on map” or “Enter an address for directions to this store” you can make some inferences about the user’s intentions to actually visit the M&S store in question.
Use of a promo code – Using an online voucher or promo code is an obvious way to combine online tactics with offline conversion (see a John Lewis example in Fig. 2 below). One can use the promo code as an event in an analytics tool and capture data on e.g. the number of codes or vouchers exchanged in-store vs the number of vouchers sent. I guess the only downside is that you’re unable to capture many interesting insights if a user doesn’t redeem her voucher or code.
Controlled experiments – Running controlled experiments was the bit in Kaushik’s piece that intrigued me the most. The idea behind these experiments is to validate retail ideas in the real world (the same as “experimentation” in a ‘lean’ context, which I’ve written about previously). As Kaushik explains, “the core idea is to try something targeted so that you can correlate the data to your offline data sources (even if you can’t merge it) and detect a signal (impact).” I’ve included some prerequisites for successful experiments in Fig. 3 below. One of them is to isolate the experiment to different states that are far from each other. As Kaushik explains, this way you are isolating “pollutants” to your data (things beyond your control that might give you sub optimal results).

Main learning point: Learning about how online can affect offline conversion felt like a good starting point for my getting a better understanding of the world of omni-channel analytics. The next step for me is to find out more about the impact of offline on online conversion: how can we best measure the impact of what happens offline on the conversion online?

Fig. 1 – Screenshot of the results of Mark & Spencer’s store locator

Fig. 2 – Sample John Lewis voucher – Taken from: http://www.dontpayfull.com/at/johnlewis.com

Fig. 3 – Some points on prerequisites on controlled experiments by (online) retailers:

Clearly defined customer segments of a decent size to quantify the impact of the experiment.
Design the experiment in such a way that the results can be isolated and compared in a meaningful way (e.g. IKEA umbrella sales on a rainy vs on a sunny day).
Random selection of customers in the control group (who get the current offering) and the treatment group (who get the experimental offering).
Clear assumptions and hypotheses which underpin the experiment.
Create a feedback loop which allows you to measure or observe how customers respond to different experiments.

Related links for further learning:

As I learn …

Looking at key omni-channel analytics – Part 1

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