Experience-Driven: 3 Ways To Turn Digital Data Into Action

Experience-Driven: 3 Ways To Turn Digital Data Into Action
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Experience-driven businesses – those that demonstrate a consistent commitment to customer experience (CX) across people, processes and technology – are turning digital data into action and witnessing positive results. On average, experience-driven businesses are achieving revenue growth of 23 percent, compared with 13 percent for other companies.

Although the benefits of this approach are clear, a co-produced report by Adobe and Forrester Consulting shows only 29 percent of businesses in the Asia-Pacific region (APAC) are considered to be experience-driven businesses. Organisations are under increasing pressure to deliver on their investments in technology and data. What prevents them achieving this? Misaligned business units coupled with an inability to substantiate insights that would lead to action.

Considering digital maturity is becoming synonymous with data maturity, let’s take a look at three essential steps to turn digital data into action.

1. Define problems and set goals.

The first stage is establishing goals. Is it a list of specific and distinct problems that the business needs to address or is it a weekly report on KPIs for which everyone needs to agree on baselines. This step should also clarify ownership and accountability for all sources, technology and data storage.

For example, a large banking and financial services firm was excited to kick start a digital insights program but just a year later, couldn’t justify it because no one knew what an insight looked like, let alone what to do with one. While there were crucial insights to be had about the firm’s conversion flow, ownership, tasks and baselines had not been set, so the team could not action these quickly with business units.

In contrast, during the program kick-off, another company developed a simple template for conversion optimisation to standardise how insights and results would look. This helped align business units and everyone knew what insights would look like so they could action them quickly.

Fostering trust is very important in this step. You don’t want to end up asking: “Can we trust this data?”

2. Collect and connect the data.

Companies tend start by creating a large store of diverse data accumulated from different systems – also known as a data warehouse or data lake. In this step, the point is not to create an all-in-one data warehouse system as this would reduce your speed and agility. Starting with a smaller and more specific data-mart will make it more agile and easier to build upon.

Don’t get me wrong, a comprehensive, organisation-wide data warehouse is a valuable, long-term investment, but it is essential to focus on the results and work on smaller data-marts that can be layered with connected information over time.

Here is an example. An e- Commerce startup in India decided to focus on optimising the last three steps of its conversion process. The company started with digital data captured in Adobe Analytics and carried out various fallout and funnel comparison analysis between its two major channels, web and mobile.

The company connected then connected web and mobile to understand how products, category, audience and offers affect conversion. After three months, the next step was to connect and enable email, push and ad-words campaign tracking to conversion, which provided further insights into where the company could best invest its marketing budget.

Finally, after a year, the company had connected offline attributes to identify offline closures and returns, which provided a comprehensive conversion analysis setup, providing immense value. This project may not have such a success had the company built a fully fledged data warehouse in the first instance for solving its conversion-optimisation problem.

3. Generate insights with a clear plan of action.

This is where you need to make sure you’re not blindly reporting everything that comes your way – for example, repeatedly creating structured reports and dashboards in Adobe Analytics. You may have a stack of dashboards, spreadsheets, multiple success metrics and automated reports hitting your inbox daily and still get the feeling you’re not truly taking the business’s pulse.

Instigate a sense or urgency and speed for generating insights rather than informal summaries. Be willing to fail early and live with “good enough” outputs – striving for perfection may be slow in producing action.

Common problems here include data discrepancies from your analytics and back-end systems, non-availability of a single customer ID, bot and internal traffic filtering, and session and visit-count discrepancies. There is no silver bullet for solving these issues quickly and you don’t want to lose sight of your main purpose: generating insights.

It is important at this stage to ask:

  • Does this analysis answer the organisational question defined in step 1? If so, how?
  • Does this insight also include pointers to defend its discovery?
  • Did you perform a quality-assurance process on the insight generated? Does it pass all test conditions?
  • Did you use any predictive analytics in the insight-generation process and compare your insights?
  • Is there any other data source you haven’t included so far? Would this be a limitation?
  • How are insights connected to action? Can you optimise and automate this process as data maturity evolves?

At Adobe Symposium India 2018, Dipayan Chakraborty, director of category analytics at e-Commerce giant Flipkart, said the company has built an insights engine to “disintermediate data from business users”. By this he means Flipkart doesn’t rely on an intermediary analytics team to print data and provide reports. Whether for marketing, planning or merchandising, insights are delivered directly to stakeholders. This is a perfect example of beginning with a clear plan of action for drawing insights from data.

To craft and operate a data-driven business that’s obsessed with action, companies need all hands on deck. People, alignment and trust form the base. Technology provides the framework, while organisational analytics and insight processes decide the direction.

It is important to foster a culture in which everyday decision making is based on data. Such a culture ensures insights are generated not only by a specialist team but by everyone in the organisation through a structured technology and process framework. It’s a game-changer – the way for experience-driven businesses to stay ahead of the curve.

Want more on turning digital data into action? Discover how businesses are empowering all employees to determine their own data-driven insights here.

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