Customer Journey Data Analysis


Why do it?

What Data is involved?

What business benefits are there?

Why are companies focusing on the Customer Journey?


“We start with the customer and work backwards”

JEFF BEZOS – foundeR, CEO of Amazon

In the digital era, consumers now seem to have endless choices and often have the option to get the exact same product or service from a number of different companies. This means companies need to be easily found and accessible, typically via digital channels, plus have the right branding and messaging and tie this all together with a great user experience at every touch point to set themselves apart. It’s at these touch points where we can measure the effectiveness of the interaction using data.

In this article we’ll take a look at an offline/online retail company example and analyse where the touch points are, what data is collected and how we can use this data to make business decisions that deliver either increased revenue, decrease costs or both.

Mapping the journey

The typical customer journey is broken into 4 or 5 phases, depending on your business type.

DISCOVERY | EVALUATION | PURCHASE | FULFILLMENT | RETENTION

Various services, systems and software are involved throughout this journey and they are all recording what’s happening. For example, when a potential new customer clicks on a Google Ad and goes to your site, it’s recorded. While they are on your site and are browsing, the pages they visit are recorded. If they make a purchase or sign up for a newsletter, it’s recorded. If they ask a question or leave a review on social media, it’s recorded. And so on. As you can see there is a lot of data and it begins to mount up quickly.

So now that we have all this data, what can we do with it?

The Data

Companies of all sizes have realized the importance of data and how they can benefit from it. Getting value from the data seems to be the hardest part. When you have data coming in from everywhere at varying velocity and volume it’s hard to track it, store it and transform it into something that is usable and the business can benefit from. (There is a whole other article on this coming up. Sign up for our updates). Often, the services and software you use has some analytics or reporting built in but they rarely integrate well with other systems so you can’t get an overall view of what’s happening and how each metric or KPI is affecting another. An example of this is the constant challenge for companies to get an accurate measure for Marketing ROI (Return On Investment) or MROI as a newer acronym. Lot’s of channels, lot’s of systems but hard to know what’s really driving revenue and whether your marketing dollars are being spent well.

Marketing ROI is becoming vitally important as the mediums for companies connecting with customers diversifies. DOMO dashboard example.

And now for the data points…


A sample Customer Journey for an offline and online retailer with associated data points and data sources throughout the journey.

In the image above, we mapped out a basic customer journey and associated data points for our sample offline/online retail company. As you can see, the amount of data or touch points is quite a lot throughout the journey and this list is not exhaustive. We found 35 individual points with up to 9 data sources. This can be tricky to manage for companies of all sizes. We’ve seen big and small get it wrong.

So how do we go from data to decisions?


A high level overview of the data ingestion, transformation and visualization process.

Data Sources

The good thing about going through the process of mapping a customer journey and the associated data points is it makes you list out all the Data Sources you have and where they fit in. Once you have this, you can then go about figuring out how they will integrate together and what are the relationships between the data. For example, if you’re connecting a clicked Google Ad to a purchase on your website shopping cart, there will be at least 4 data points involved. In this example it’s Google Analytics, Google Ads, Magento (ecommerce) and SAP. This will vary depending on each companies environment.

Data Transformation

There are a few options in the Data Transformation phase and the decision is often dependent on company size, complexity of systems and budget. You can create a Data Mart, Data Lake or a Data Warehouse to host all of your data centrally or you can pull data on the fly each time you want to look at it. All options have their pro’s and con’s which we we’ll discuss in another article. We typically recommend to think big but start small.

Data Visualization enabling business decisions

Now that we have all the data we need in one place, we need to visualize it and present it to the business so it’s easy for them to consume and make decisions with confidence. This is where dashboards and visual analytics come in. They give you a graphical representation to enable quick decision making and the ability to filter and drill into the data to see what’s underneath and what might be driving certain outcomes.

Example products and vendors: MS Power BI, Domo, Tableau, Qlik etc.

Most dashboards are metric and KPI driven, meaning the data that feeds into them is related to a business question or a business function’s key responsibilities. For example, the Marketing department might want to look at their MROI. The end result is made up of a number of metrics that come from our newly transformed and joined data. The typical approach is to work with the business unit and ask questions of the data and display them in logical collections of charts/graphs etc. that are informative and actionable.

As customers and the data being produced by them moves so fast in the digital era, taking advantage of opportunities within a small time window can be a key competitive advantage. Having timely access to the right insights enables companies to take advantage of those opportunities.

What kind of questions can we ask of the data?

A sample of questions below:

What steps did the customer take before making a purchase?

Find out which path/steps are working, and which may be able to be tweaked to gain better results.

Which steps in the journey are performing well, or not?

If you find you’re losing potential clients at 1 or more points, maybe the check out on your online store, this is an area for improvement. Some low hanging fruit is to focus on where you’re losing your customers. Start with the highest loss rate data point and work back from there. Reduce loss rate, increase likelihood of a sale. Make changes and review again after you have more data. An iterative loop will form and that enables continual improvement.

When is the best time to interact with a given customer?

You put a lot of effort into your website, content and social but when the potential clients sees it can have a big impact on their conversion rate or likelihood to take next steps.

What is the best channel to interact with the client?

Knowing what is working is important (via website, chatbot, email, phone etc.). Also knowing where you have weak spots is of equal importance. Some channels have a high Customer Acquisition Cost (CAC) while others may need some work but can be just as effective in converting prospects into customers. Lower costs, higher revenue.

Which kinds of paths do clients take before becoming loyalty clients?

For loyalty and repeat business, some customers will take a certain path or complete certain actions in a pattern. If you identify these paths or patterns, you’ll be able to predict the likelihood of another customer becoming a loyalty member and offer them an incentive. Once your data is good, you can apply machine learning and predictive capabilities too. This feeds the iterative, improvement loop.

What are the Business Outcomes?

What are some business outcomes you can expect by having timely data access and actionable analytic dashboards?

Increase in New Customers

Which products are winning customers over? Which advertising channels are getting the best ROI in terms of Customer Acquisition Cost (CAC)? Maximise your spend to get more customers for less.

Upselling New and Existing Customers

Which products are customers typically buying and what are the relationships between the products? A timely offer with the right product increases upsell rates.

Lasting Loyalty for Existing Customers

What is the behavior and demographics of your most loyal customers? Are they influencing others? An existing customer is a much easier sell than a new one.

Reduce abandonment, drop outs

What are the characteristics of the low performing or disengaged customers/loyalty members, other than low/no sales? What offers or content have the best results bringing them back? You spend money to get them there, if you reduce the abandonment you’ll win more often.

In Summary

As you can see, data can be used in powerful ways to enhance the customer journey and if you can measure it, you can improve it. Many companies also use the data they accumulate throughout the customer journey to forecast future sales. This is because having a great (or failing) customer journey can have a big impact on future outcomes.

Some companies are concerned about diving into the digital customer journey as it may be too big of a task and they simply don’t get started or, they’ll often get stuck at the data integration hurdle. But with the amount of tools and services available on the market now and the price of these always coming down, it’s within the reach of most companies, large or small. The rewards for getting the customer journey right across all the phases can be exponential for some companies and the ones that are getting it right, are winning.


If you would like to find our more or speak to the author of this article (H. Whitehill) then complete the form below.