Predictive Analytics – The Next Evolution!
Today, SPAR retailers produce more data than ever before with systems such as SIGMA storing volumes of data such as stock information, sales, POS transactions and more. This data is often so vast however that it is hard to understand, report on and, most importantly, make incisive decisions on. Because there is more and more competition in the retail environment, retailers are more hard-pressed to convert information into insights that give them an advantage over their competitors.
The future of business intelligence is here to help – introducing predictive analytics! Traditional business intelligence tools are backwards looking and reactive in nature. Retailers are presented with their data and then have to spend time understanding the data presented. Once this is done they are then able to make decisions on the data and have to hope that the data is no longer outdated and invalid. It beats making decisions based on guesses but is that good enough? If your report suggests that there was potential point-of-sale fraud during the previous week, there isn’t much that you can do with the losses already incurred. Predictive analytics addresses this by using data in the system to predict potential future events – thus allowing you as a retailer to act proactively. So what are some of the uses of predictive analytics?
How do you know whether your promotions are great for promotion hunters but terrible for your loyal customers? While predicting product sales is critical, predicting sales by customer segment is essential. Predictive analytics helps retailers to develop customer segments, and lead to a better understanding of the preferences of those customer segments. This allows retailers to set promotion campaigns that target the most valuable customer segments. If you can’t predict your performance by customer group, you are throwing promotions against a wall and hoping some will stick
Retail is being especially hit hard within the supply-chain, as greater complexities resulting from increased automation have given fraudsters new avenues to exploit. Fraud can be found both inside and outside an organization. Suppliers can manipulate delivery orders to take advantage of the system. Employees may have a scheme in place to use point-of-sale transactions to defraud the business. No matter where fraud is occurring, retailers can use predictive analytics to pick-up on trends and alert the business to potential fraud before incurring massive losses as a result.
Predictive analytics can give considered answers to several questions retailers often have regarding their product pricing:
- What is the correct price point to maximize sales?
- What is a market price of the product?
- What would be the impact of competitive pricing on sales?
- How often to run price-based promotional activities?
The use of predictive analytics to analyse pricing can show return in a matter of months giving an immediate and tangible return on investment.
Predictive analytics helps retailers remove the uncertainty factor from Inventory Management. Predictive analytics can remove the costs that come with holding slow-moving inventory. Predicative analytics will remove the need to study volumes of SIGMA product sales information and rather alert the business to products lines that are understocked and more. Predictive Analytics accurately predicts demand and suggests better replenishment strategies. This helps reduced tied-up capital and ensures you stock exactly what your customers want!