Most retailers are already engaged in data analytics and predictive modelling. Today, using data to predict more successful outcomes – ultimately leading to increased sales – is not as mystifying a concept as it was even a year ago. But with a market being upended by an omnichannel imperative and consumer demand for instant gratification, how can e-commerce retailers continuously and dynamically test out business models and strategies, refresh their questions and findings, and find out what works, particularly when it comes to pricing and promotions?
Advances in AI have a role to play here. Machine learning is very powerful in point prediction, point estimates in price – the goal being to make sure you are dynamically pricing. And it’s very effective when pricing at speed. It can help improve recommendations based on a continuous learning loop and it’s particularly applicable in price and promotion optimisation.
But the biggest challenges in pricing and promotion currently are speed and innovation. Yesterday’s pricing strategy is not going to work for tomorrow, so you have to constantly reinvent yourself. Data analytics requires a focused business strategy in order to deliver positive results, one that will allow retailers to constantly reinvent different ways of capturing and segmenting their data, analysing it, creating new products and testing product promotions in order to stay afloat.
This challenge is further complicated by the need for retailers to have an omnichannel strategy, because online crossovers may not yet always work in the store. However, when it comes to truly differentiating themselves in the market, retailers need to figure out better and stronger ways of connecting one-on-one with the consumer. And connecting the dots to accomplish this, the best approach is to employ a combination of four strategic tools.
Four pricing and promotion tools that need to work together
Though volume-driven retailers such as Starbucks and Dunkin’ Donutsare models of pricing and promotion success, the same principles apply to any retailer. And while the hospitality industry has always led the pack in terms of pricing and personalised promotion, many industries are now learning from each other.
At the core of strategic success in pricing and promotion are an omnichannel presence, a loyalty component, a promo and coupon programme and some level of personalisation. All of these can be advanced by smart use of data and predictive analytics. Let’s see how this works.
• Omnichannel. Whether a retailer’s business goal is to increase overall margin, grow the shopping basket in the aggregate or more granularly test one new product in a specific region, it’s now become table stakes to have an omnichannel presence. Data analytics can help retailers by helping to speed innovation and merchandising decisions.
• Loyalty. Taking the example of business travel, loyalty programs can vary by region, by market and of course by company. Nonetheless data analytics help to move these loyalty programs toward a more inclusive promotional view of the specific customer by automatically pulling in additional promotions and personalization.
• Promos and coupons. Getting that coupon upon checking out from Barnes & Noble for a free cookie from the café is a certain incentive for another visit to the store. Data analytics has helped target promotions for many retailers, driving both online sales and in-store traffic.
• Personalisation. Retailers have gotten creative about using many available channels to personalise their offers. For example, a number of small restaurants in Chicago post messages on Instagram saying “Tag your friend and you’ll win a free burger”. It’s a simple concept but it exemplifies both personalisation and social self-promotion; and at the end of the day, the customer gets something for free. Without data analytics, this would be impossible.
Using a combination of these four tools should help any retailer stay relevant, and predictive analytics plays a role throughout.
Mike Kim is a Director at AArete, a global consultancy specialising in data-informed performance improvement, and heads its Center of Data Excellence. He can be contacted at firstname.lastname@example.org.