“If you torture the data long enough, it will confess to anything” – Ronald Coase, British Economist
The potential to leverage data and plan thoughtful marketing strategies has always been undermined.
Big data has always been a powerful technology to uncover consumer behavior.
Analyzing data for insights can be a never-ending cycle. A decade ago retailers never took sales data seriously and sold it away to other companies to buy back the insights.
That pattern has changed now.
Modern-day retailers strive to analyze new kinds and volumes of data up to the level of granularity to the individual customer to stand out from the competition.
Case-in-point: Kohls for instance is has been embracing the digital journey focussing on a data-driven approach by introducing segmenting Generation X and Millenials, and understanding thier behavioral patterns. Looking at the data, they have learned that Gen X customers would still like to chat with a customer on the phone, but not the Millenial market. AI powered chatbots can very well suffice the need and deliver a more cost-effective, enjoyable experience to this particular segment.
Single view of the customer(SVOC) is a very well-known retail function where all the historical purchases, marketing communications of all their customers across all channels are aggregated, in a form of a dashboard. And this can be an ideal place to start a data monetization strategy. It could be as simple as sharing the individual customer insights to the marketing teams or highlighting the relevant content based on historic conversations with the customers. Best results of running centralized analytics on integrated databases would only be seen when marketing starts to implement these actionable insights on a real-time basis.
A survey from RSR, retail systems notes that 90% of the high performing retailers focus on gathering not only sales data but also behavioral and sentiment data to learn more about the customer. Enabling customer journey with personalized, relevant shopping experiences and tailoring assortments has become a very high priority, particularly for the eCommerce channels.
DataProm, a Ukrainian startup marries e-commerce and big data with mathematical analysis methods and big data algorithms behind the scenes. Founder Anton Vokrug, plans to penetrate Asia to help eCommerce companies deep dive on buying trends and consumer behavior patterns.
Both, transactional data and non-transactional data can be valuable sources to analyze. Once that is done, it is very important for retailers to correlate social behavior to sales. And the best way for retailers to learn this starts by asking the right kinds of questions.
As data gets more complex than ever before, retailers also need to change their way of doing predictive analytics to get arms around this problem. Paying very close attention to Cognitive computing is the most important technology investment in retailer’s portfolio.
Commerce insights, a merchandising offering from IBM sets the stage for a cognitive experience where pro-active alerts are shown powered by NLP (Natural Language Processing). It not only shows the gaps in assortment data but also explains the problem, displaying product anomalies and then makes recommendations so that retailers can understand, build confidence and take corrective action accordingly.
Pricing is another area where data sources of historical sales can be a goldmine to analyze to make informed assortment decisions. Lowes relies on technology providers like Boomerang commerce in the US, Revionics in Canada and Upstream for its Mexico business to help them define their pricing strategies and assortment selections. All the 3 technology providers heavily analyze huge sets of retail data, coming up personalized pricing strategies that would best help the retailer’s operating margin.
Data-driven platforms help retailers to directly see the optimal price points across different categories and quickly adapt prices to the changes in the market.
Imagining all the fascinating possibilities make consumer internet and retail even more exciting.
Innovating in the consumer analytics space can be a never-ending vicious cycle.
Key Take Aways:
- Are you making data-driven-decisions at work?
- If not, how can you influence that decision making process?
- Putting data at the heart of the business strategy:
- What are the 3 most important metrics that your business or team must closely measure?
- Finally, who are your target customers and what are their true needs?