Creating valuable insights in times of uncertainty

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Is your business properly fixated with its relevance to your customers? Do you really know who they are, and what drives them? Do you truly understand their needs, triggers, barriers, preferences, and influences? One thing is for sure. The COVID-19 pandemic has brought unprecedented disruption to customer behaviour, making business intelligence more fundamental than ever to your success.

Take a quick look at the marketing activity of a handful of global retail brands, and you’ll be amazed at how many are under-informed about their customers. About the current needs and future aspirations of those customers. And about the performance of their marketing spend. Many don’t even acknowledge the comments of their customers on social media. This is difficult to comprehend, especially when this information is so easy to obtain, when there are tools to connect it all together, and when there are models to predict future outcomes.

What is the disconnect in harnessing actionable business intelligence?

Sometimes it seems like retailers have gone backwards from the 1950s, when local shopkeepers orchestrated a wealth of intimate customer knowledge, without the help of data science and attribution models. Often, the reason for this is that retailers simply don’t have their data, tools, and talent working together effectively. A recent survey of Chief Marketing Officers (CMOs) reported threefold increases in marketing analytics budgets, yet 60% of marketers surveyed did not feel they had the right tools to properly impact marketing performance.

Of course, customers are more demanding than ever before. They continue to embrace the opportunities of their connected lives, taking to new tech like fish to water, and always expecting a unique, sublime experience. Yet customers remain proud of their low attention span. They don’t remain loyal to retailers, knowing they need us more than we need them. And, unsurprisingly, customers are experts in holding brands to account, always sharing the details of their bad brand experiences. We might assume the problem is customers’ unwillingness to share data. In fact, to the contrary, they’re usually happy to share information, so long as its use is transparent and mutually beneficial.

Let’s dig deeper into the issues

Of course, the customer journey is more complex than ever before. For example, how do retailers track customers from owned websites, to third party sites, back to social media, into a store, and back online? How do retailers measure insights, optimize them in real time, and make sure they get their marketing mix right? Even retailers who have managed to optimize data, technology and operations, often face three fundamental flaws:

  • There’s a tendency to over index, focusing on channels and not audiences
  • Too much time is spent on planning and reporting post event, compared to time spent on optimizing, testing and simulating
  • Data, technology, and operations are not in sync or working in harmony.

Setting up for success

To counter these fundamental flaws, we’ve identified a simple five stage plan that can properly align your business and create competitive advantage. None of these will be surprising but, implemented well, will add tangible value to your operations. Here is a brief summary of the five stage plan:

  1. Questions to answer – hypothesis building

The first step is simple, but often overlooked or sidelined. You need to scrutinize exactly what data your business needs. A key challenge here is understanding exactly why your business needs this data. Likewise, you need to understand how to harness this information to provide value. This step often also de-clutters the reams of data we collect and never use.  For example, a German drugstore brand recently wanted to confirm whether their online growth during the pandemic was due to new customers, or to existing customers moving online. Once they identified the majority as new customers, they focused their marketing efforts on tracking the value of these relationships, and on shifting marketing spend to attract more new customers with a similar profile.

  1. Data management

Once you’re clear on what you need to know and why, the second step is to identify the data sets you’ll need, across both owned data sources, and partner data sources. You won’t need a complete, fully loaded data model to start with, but this is where you would ultimately connect the customer journey using customer, media, sales and third party data. The big challenge is often linking online and offline. However, there are many possibilities, including the use of location apps to track customers, via their smartphones, to your retail locations. Alternatively, you can monitor online check-in data, triggered by the use of a payment card, loyalty card or interaction with a digital device or digital product.

  1. Decision Engine

The third step is to create the capability to analyze this data, then model scenarios, build segments and create attribution models. This will allow you to do two things. First, you can take action against the insights. For example, if there is learning linked to the in-store shopping needs of certain customer segments during the pandemic, you can directly address the issue, tracking and measuring impact. Second, you can create campaigns directed at particular audiences. For example, a recent campaign for a UK grocer analyzed the products customers had previously searched, viewed and bought, and then alerted them when essential products became available during the peak of the pandemic.

  1. Architecture and data

Step four is all about connecting technology and data. Because the first three steps will only work if you’ve established the supporting architecture and data models to connect the appropriate technology and data. This will allow you to understand all data sources, and how data is collected, integrated, and stored. And it will enable you to optimize the analytics tools and products across market research, search, social media, product data, sales data and third parties. Connected and working together, these enable real time analysis and decision making. Over time, this can be enhanced with machine learning, AI and more advanced analytics, but building the right foundation of architecture and capabilities is key.

  1. Operating model

Finally, step five is to ensure you have the optimum operating model to manipulate the data and drive business value. As the saying goes, you may have the Ferrari, but you also need the driving skills to enjoy it. Key skillsets include data scientists, analysts, visualizers, and storytellers, who can use the analytics engine to deliver rich insights, decision making, and in-campaign optimization to drive brand growth.

Summary

Our simple five stage plan doesn’t need to be implemented completely or immediately. But by focusing on the big impact areas, you’ll lay solid foundations you can build on. As we brace ourselves for subsequent waves of the COVID-19 pandemic, a critical analysis of customer activity is business critical. Understanding customers’ evolving shopping patterns, in terms of category, channel and frequency, is key to being in pole position to meet their needs.

The immediate need is for a critical Q4 analysis of gifting during the coming holiday season. How will customers buy gifts and have them delivered? A recent survey reveals that over 90% of gifts will be ordered and delivered online, putting massive strain on carriers. By optimizing this information, retailers will be able to model how they best build capacity and align to customer needs. Only finely tuned businesses will survive. Now is the time to get your insights capabilities into shape.

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