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Friction, spark, innovation! The formula for breaking big data sets to impact customer experience

Technology & Data

Friction, spark, innovation! The formula for breaking big data sets to impact customer experience


Holly Joshi outlines a three-step framework that, if used correctly, promises happier customers, a more effortless journey and overall positive rewards.

The story is a familiar one. After chasing down the path of the optimisation journey you’ve corralled your data. You’ve accumulated layers, databases, cubes, and mounds of data at your fingertips and yet are struggling to know the answer to, ‘Now what? Now that I have data how do I break it into the right data set and use it?’

This position is a common predicament. How do you find the insight on which to iterate and innovate? How do you keep onward and tackle it, harnessing the data versus it being a useless set of numbers in a thick PowerPoint deck?

Most importantly, how can the right data spark the realisation of where to hone in your testing and use it to innovate on your product and customer experience?

The simplest path to break into the right data set and take action is to relate the seemingly unrelated. Huh?

By identifying and correlating three key inputs within your business intelligence you can calculate ‘friction’ points that spark product innovation and elevate your customer experience. The formula is easy:

The target + experience map + analytics = find the friction, innovate, and pivot!


The what (three key inputs)

  1. Target: a target decided on by filtering through KPIs, roadmaps and revenue,
  2. Experience map: a qualitative customer journey map, cross-channel, and
  3. Core and Social Analytics: a quantitative customer path.


The how

First, find the ‘target’. Round up your specific and strategic business goals. Connect with a small group of influential stakeholders and the product team to act as a collaborative sounding board.

Aim to narrow this set of goals down to one or two primary ‘targets’. Revenue will always be in the shadows, so rally around a more customer-centric metric – and one more easily shared across your organisation – to gain buy-in. For example, choose absolute unique customers, engagement, or basket size.

Second, connect with your user/customer experience and design team and get your ‘experience map’. Find all the points your customer interacts with your product on/offline, and specifically on your site – don’t forget email and customer service communication points.

Attempt to get the full picture of start to finish interactions in both on/offline instances. If at first complete end-to-end seems overwhelming, you can break this into snackable pieces. Take one piece at a time, hone in on your site, then the journey of an offline store, then stitch them together to get the end-to-end view.

Third, gather your core ‘analytics data’ (funnels, journeys, conversion point, etc), your social data, and any data you track on your content (views, usage, referrals, reach etc). Weave any of the quantitative values together that you can. For example, the sentiment from social analysis discussion at the same stage you see exit rates (abandonment), is a vital point in connecting qualitative and quantitative information to infer where your customers might be challenged.


Connect the dots

Equipped with these three prime inputs, get the stakeholders and owners of those paths in a room and literally layer one path on top of another. Friction points will surface either multiple intense and high volume friction areas, or repeated friction within one area. Once you’ve done that you find the exact hotspots to know what data metrics are the most important. Then set up an A/B testing roadmap and start to innovate on those results to enhance your product and experience.


The hard part (the caveats)

The three-step framework is easy to describe, and gives measurable outputs, but do not underestimate the effort to get the inputs right. They must be detailed and correct otherwise they are useless.

For example, you cannot take offline store journey metrics and try to pair them against your online (e.g. Google Analytics) data. The risk introduced is too much hypothesising and forecasting. This may mean you need to get physically into your store and observe customers and track manually.

In sum, while the data mounds seem daunting, simply break it down. Connecting the three inputs provides a robust view of where your customers are struggling and what is causing poor interactions with product and/or experience. Doing your due diligence with these inputs, layering them, finding the friction points might lead to surprising results, but definitely promises happier customers, a more effortless journey, and overall positive rewards.



Holly Joshi is senior manager, optimisation and analytics APAC, at SapientNitro.


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