A lot of retailers already use both data analytics and predictive modeling today. Indeed, it is pretty clear that the more data that is used to make predictions, the more successful companies are. However, since the market is so chaotic as omnichannel is so aggressive and consumers want instant satisfaction, how are ecommerce retailers able to constantly as well as actively try out both business models and strategies, freshen up their questions and conclusions, and figure out what actually works with regards to pricing and promotions?
Technological AI advances can help out a lot. For example, machine learning is a great resource for point prediction, which estimates price, and at the heart of this process is the goal of pricing your items dynamically. This is very helpful if you are looking at pricing at speed. Indeed, your recommendations will get better as they are based on a constant learning loop, which especially helps with both price and promotion optimization.
However, one of the biggest problems that companies are faced with when trying to set the right prices or promotions is both speed and innovation. Indeed, the pricing strategy that you had used yesterday will not help you out the next day, therefore, you need to continuously remake how data will be gathered and segmented in order to be analyzed so that new items can be made and new promotions can be tried out to stay in the game.
The issue gets even more difficult when retailers are looking towards getting an omnichannel strategy as online crossovers don’t always work inside of a store. That being said, if retailers really want to differentiate themselves within the market, they have to learn better and more powerful ways of making one-on-one connections with their shoppers. The best way to do so is by combining four strategic tools.
The Four Pricing and Promotion Tools That Should be Combined
Although those retailers like Starbucks and Dunkin’ Donuts, which are driven by volume, are often looked at brands as who have had success through their pricing and promotion, any retailer can reach their same level as they aren’t exceptions. Indeed, despite the fact that the hospitality industry has always been the leader in pricing and personalized promotions, a lot of industries are making a lot of progress by learning from one another.
At the heart of strategic success in promotional pricing is the presence of omnichannel, promotion and coupon programs, an element of loyalty, and some degree of personalization. Every single segment can be enhanced with the help of the proper utilization of data combined with predictive analytics. Let’s take a look at how everything works.
Regardless of whether the objective of a retailer is to boost the margin as a whole, expand the shopping basket in the aggregate, or test out an item more intensely in a certain area, it is imperative to have a presence in omnichannel. Indeed, data analytics are able to help out firms by increasing the pace of both innovation and merchandising decisions.
Promotions and Coupons
It’s clear that when you get a coupon for a free cookie as you check out from, for example, Barnes and Noble, this is an incentive to go to another shop. Through data analytics, retailers have gotten better at promotion targeting causing their online sales as well as their in-store traffic to increase.
Similar to business travel, loyalty programs can vary based on region, market, as well as the company. That being said, data analytics can ensure that the promotions are more inclusive towards each specific customer by automatically using more promotions as well as personalizations.
Retailers have become so much more creative with regards to using a lot of channels in order to personalize the offers that they have. For instance, a handful of little restaurants in the Chicago area create posts on Instagram with the message, “Tag Your Friend and you’ll win a free burger”. As simple as this idea is, it demonstrates personalization as well as self-promotion while also providing a customer with a free burger. This would not be possible if it weren’t for data analytics.
By combining these four tools and utilizing predictive analytics all throughout, not a single retailer will struggle with being relevant.