Tapping the Untapped Market, In today’s fast paced, ever changing and highly competitive retail world, retailers have to rely on business insights in order to maintain an edge over its competitors and attract more customers. Customers of today have a different expectation from retailers – “I Want It When I Want It”. Data is the key to gain insight on “what” and “when”. But with the wealth of data that is available, extracting the right data from the right place and turning it into useful information has become a major challenge. Another challenge is eliminating the time warp. For sustaining that competitive edge, retailers need to stay ahead of consumer demands for which business insights are again crucial. Traditional data management tools take time to publish the results due to in-grained data governance structures that the inventory is stale by the time it is available to the consumers. So organizations frequently face data fatigue. With the shrinking “decision windows”, business decisions have to made fast and right.
Typical data elements in retail industry include transaction data from point-of- sale (POS), web and mobile sales data, loyalty data, market basket analysis etc. all of which are structured and resides within the organization. With the advent of social media, retailers are looking for retail solutions that can harness data from social media sources like social media, blogs, online forums to understand market trends, competition and customer sentiments.
To reap the power of this constantly changing but relevant data, which can be both structured and unstructured, Master Data Management (MDM) solution of the future requires capabilities to add or modify business and governance policies dynamically. This dynamicity can drastically reduce retailer’s time to market and thereby eliminate lost opportunities or dollars. Apart from the data sources mentioned above, in our blogs, we will be exploring other data sources which can be used as a trigger to make the data governance model dynamic.
Weatherization is the process of identifying, analyzing and applying weather intelligence to provide measurable and quantitative returns across an organization. Meteorology can influence a customer’s visit to a brick-and- mortar store or website. A new area in analytics that has been uncovered which can support evidence-driven decision making for retailers is weather data. This is specifically useful for those who carry seasonal product lines i.e. range of items that has a limited sales window and which is driven by local weather.
Weather can have both evident and mild effects on shopping patterns. A snow blizzard can shoot the sales of snow shovels. A hot and humid summer can boost demand for soft drinks but dampen the sales of jackets. By using advanced analytics to process historical, current and forecasted weather data, retailers can predict the shifting demands in their merchandise. Retailers can tie weather forecast projections to their customer behavior based on past sales trends in order to get the right products, in the right regions, on sale at the right time.
Most retailers exclude weather from their demand planning process without realizing that seasonal products’ demand can increase up to 100% and possibly more in any given market in any given week. But recently, there have been evidences that showed a shift in this behavior. Some retailers have started using weather data to bring efficiency into the planning process. E.g. IBM’s Big Data and Analytics Hub recently helped a leading coffee retailer to use weather data to manage its marketing campaigns and the client saw a $44 million increase in incremental marketing opportunities.
Optimizing marketing campaigns is just one of the many opportunities that can be untapped using weather data. Weather rules to regulate retailer’s data governance in MDM solution is another such application so that right inventory can be pushed to the stores when it is needed the most. To handle dynamic data governance based on weather, retailers need to capture the following data points
- Seasonal item category information: Retailers have to identify a means to categorize the information to various seasonal buckets e.g. add a keyword to the specific items. This is to ensure that one or many categories that spans across the merchandise can be selected for a particular event.
- Geography data: Country-> State-> City data as certain weather trends can be geography specific.
- Sales: Past sales trends to identify the categories that need to be classified as seasonal.
- Lift to be applied to a category: Lift defines the percentage of increase in items to be pushed to the stores to meet the demand. E.g. It was discovered that hosepipe sales increased as it became warmer, but only to a certain point. So the percentage of increase should be adjusted using lift.
- Time period: Ability to choose the time period for which the rules should be applied.
In my next blog, I will be talking about other factors that can be considered to make your data governance model dynamic.