Black Friday has firmly established itself as the biggest event on the shopping calendar with retailers keen to boost their sales in the build-up to Christmas. Those who are using Artificial Intelligence (AI) to personalise offerings and move stock and inventory are set to capitalise on the Black Friday phenomenon.
Last year, Black Friday generated around $5 billion in sales in the US alone, according to Adobe. Including Cyber Monday, this period has become an important indicator of consumer spending and how sales are likely to perform over the Christmas period. However, this increasingly competitive part of the year has forced retailers to heavily discount stock (up to 30%) making profit margins smaller than ever before.
New technologies are enabling retailers to enhance the customer experience across touch points. In order to secure customer loyalty during this lucrative period those using AI to personalise offerings based on purchase history are getting ahead of the curve.
Personalisation and relevancy
Customers expect a higher level of personalisation when they interact with various touch points and this is largely due to the volume of marketing communications they receive.
For some retailers, their definition of personalisation is simply no more than a “Hi First Name” in a blanket email campaign with generic offers or tailoring the offers to fit a broad ‘customer persona’. Unfortunately, this has been a standard practice for many marketing departments for a few years now. When you think of personalisation, think relevancy.
The problem with the ‘blanket approach’ is that customers don’t always (read: ever) fall into clean categories. Many factors can impact what a customer is interested in being offered. No retailer will see increased sales by adding a customer name to the start of an email when the actual content is not relevant and contains nothing of interest for that person.
To take a simple example, if ‘John Smith’ bought a TV in last year’s Black Friday sales, he is unlikely to buy a new one this year, so promotions sent to John during the Black Friday period, referring to TVs is quite simply a waste of time and resource.
This approach can deter your customers when they see offers that do interest them, likewise if an item is out of stock or only available in the wrong size by the time the customer reads the promotional email, they are left feeling frustrated. This disappointment can be amplified during Black Friday, when consumers partake in the shopping frenzy, only to find the item out of stock by the time they arrive can damage reputation irreparably.
Companies like Amazon and Netflix have changed customer expectations in relation to personalised curation and suggestion tools, but retailers who have implemented AI technologies are seeing the average transaction value increase.
Retailers can therefore take advantage of technology to offer personalised sales messages that benefit the customer, offering VIP Black Friday previews, relevant promotions and offers that appeal and help solve that customer’s challenges. Personalisation in time will encourage brand loyalty and repeat sales if done correctly.
Predictive analytics for efficient and accurate intelligence
Advances in machine learning, the modern application of AI in retail, have made predictive analytics more efficient and accurate. This is achieved by teaching software using historical data sets so that the machine can identify trends to a greater degree of accuracy and over a broader set of individuals than a team of humans is capable of.
Examples of machine learning in our everyday lives include Netflix’s recommendation engine, that takes user behaviour and programme content data from their 100 million subscribers to offer personalised recommendations that boost customer retention.
Another example of machine learning comes from Facebook, which uses machine learning technology to recognise the face of your friends to automatically tag them in uploaded images. DeepFace has studied the data of more than 4 million Facebook users to recognise human faces to a high degree of accuracy.
During the Black Friday period, retailers are required to manage inventory and distribution as efficiently as possible to capitalise on the increased demand and to ensure they are not left with unsold stock sitting in the warehouse.
Machine learning can enable systems to look at historical records both on a broad level and down to the individual consumer level to predict the necessary stock levels in each store to reduce stock wastage.
To highlight the advantage of machine learning, take a simple example. Item A sells 100 units in Region X, while only selling 25 units in Region Y. This could be due to many factors, including general climate and the weather, local tastes and culture, local promotions, and countless other factors.
Over a set of historical data, a system with intelligent machine learning capabilities can start accounting for these factors, both seasonal and ad-hoc, to predict future sales and make suggestions to optimise stock movement and cut out waste.
Retailers adopting this technology can boost sales and prevent unwanted stock overflow, by maximising the relevancy of their products through analytics. This means that their customers receive highly personalised products and services that are unique to them.
Mobile-first is the new norm
Conversion rates for purchases made via a mobile browser are stagnating around 2%. Compared to traditional e-commerce websites, browsed via desktop, which have generally flatlined around 4% conversion for several years, mobile e-commerce is seen as the least effective sales channel.
One sales channel has seen consistent year-on-year growth. Business apps had conversion rates around 6% in 2017. More and more retailers are beginning to understand the opportunities created by having a unique mobile app to engage with customers, capture data and create targeted campaigns to drive sales.
Mobile apps provide customers with nifty features such as the ability to scan labels, bookmark favourite stores and, of course, order items. Purchases made on mobile devices totalled $2 billion sales in the US in 2017 alone.
Customers are increasingly using the convenience of their mobile devices to avoid the inconvenience physical hustle and bustle of the brick-and-mortar store, and during Black Friday this becomes an increasingly important segment to tap into.
A customer may avoid Black Friday because of the sometimes-chaotic nature of the sales period and become a lost opportunity. But by offering an alternative way of shopping, that customer receives a more convenient shopping experience that caters to them, it also allows a retailer to maximise their sales that would have otherwise been lost.
Mobile apps have the secondary benefit of allowing a retailer to have more direct communication channels with their customers. Services such as Instant Messaging (IM) and push notifications are effective ways of keeping in touch with customers who have the app, and also to offer direct and relevant promotions to a customer’s phone.
Mobile apps are becoming the new preferred buying channel and retailers who do not offer an app to their customers will soon be behind their competition.