3 Ways AI will impact Retail

It is clear that AI is going to drive fundamental changes in Retail. I’ve been fortunate to spend the last few weeks talking to retail leaders, connecting with innovative tech suppliers and attending conferences, exhibitions and seminars.?My main purpose was to understand how AI is going to impact the retail sector and whether all the hype is true and not another fad (remember the Metaverse or Augmented Reality?). Whilst there are numerous use cases and benefits, in my conversations, 3 key areas stood out for me:

1.???Marketing

Generative AI is likely to make a relatively quick impact here – a significant number of generative AI tools are already available and many more released on a daily basis.?Content creation is becoming faster, easier and cheaper. It is only a matter of time before the content becomes as good as, if not better than, what humans can do (and it’s close already). LinkedIn often has posts from AI evangelists referencing tools across text, images, video and audio generation.?I’ve seen an example of a firm create their entire marketing campaign content for a webinar from AI tools, not tweaked the output and managed to drive an amazing turnout. ?Key impacts

Customer:

Deeper personalisation – with the ability to produce large amounts of content with reduced resource challenges, content can be tailored to micro segments rather than the broad customer segments leading to improved brand loyalty, customer retention and sales. Retailers will be able to communicate with customers quicker and in a more relevant way than they can today.

Retailer:

Costs – marketing teams will need less resources from ideation to creation. Expenses associated with agencies, photography, editing etc. will be reduced. This could lead to savings or reinvestment in incremental activities – either way there will be a benefit to the top or bottom line

Skills and Capability - retailers will need to ensure that they have the skillsets, culture and technology to collect and process the required data and generate the content in line with their goals and values.?Prompt engineering in the short term is likely to become a critical skill!

Market:

Smaller players - brands and businesses will be able to create what budgets would not allow in the past.?Whilst media access costs remain the same, AI will not only help level the playing field in content creation but also optimising returns on investment by providing real time insights into customer behaviour and preferences, enabling retailers to learn quickly and create more targeted and effective marketing campaigns.

?2.???Customer Services

An existing trend that will continue to get better and more human like. The use of AI-powered chatbots and virtual assistants is providing retailers with 24/7 customer service capabilities that will continue to grow (and get better). By automating customer service, retailers can provide quick and efficient support to customers, improving the overall customer experience, increasing loyalty and reducing costs

3.???Predictive Analytics

Predictive modelling has been around for a while but is getting better.?By analysing data, AI can predict future trends with a higher degree of accuracy. There are a number of interesting use cases

Stock Management - Predictive modelling can optimise stock for retailers. Retailers have always tried to stock items based on what they know about customer behaviour based on the time of year, weather etc. They’ve done this with the help of sales trend data and a good dose of experience often with varying degrees of success and consistency. Predictive models, based on years of data and a variety of different sources and external inputs, offer retailers the chance to do this with exponentially greater precision, detecting nuances in consumer behaviour that would escape the notice of even the most perceptive retail leaders (and excel spreadsheets).

For example, rather than solving generically for seasonal summer items, a deeper analysis can be undertaken of what has historically triggered a shift in consumer behaviour (e.g. 5 sunny days in a 7 day period) as well as longer term weather forecasts and lead times required for stock to be received, to identify the optimal time for changing stock

Social media feeds can be analysed for events that may be taking place (e.g. a concert or a local sports tournament) and a combination of historical sales patterns from similar events – possibly at another store location - or current online searches can be used to identify expected changes in demands.

Longer term trends can also be analysed and isolated e.g. changing local demographics and ranges adjusted accordingly

Staffing Schedules – Linked to the above, AI solutions can help businesses manage rotas and seasonal staffing better, enabling a more structured schedule and more efficient colleague use. This means colleagues aren’t overworked, and customers are adequately served.?

For example, in conversation with Mukesh Singh (founder of ZopSmart, a very innovative Retail Tech vendor), they have helped retailers dynamically analyse multiple inputs to update staffing schedules. These range from staff skills; planned absences; on-shelf availability; trends and volumes in on-line orders (which drive the need for picking, packing and delivery) vs. in-store sales (which drives the need for till operators).

As a result, store managers are being pro-actively advised to re-allocate staff across roles and shifts. Rules can also be set up to allow for staff to be automatically notified of changes, without manager involvement.

Price optimisation - AI powered price systems are helping retailers optimise pricing strategies and improve sales and margin. By analysing customer data, historical sales, competitor pricing, and real-time information such as sales in the last hour, inventory and weather, AI can identify optimal pricing levels by SKU, store and channel at a speed and accuracy that would not have been possible historically. Coupled with simple, low-cost solutions such as electronic shelf labels, these pricing decisions can also be applied instantaneously.

Home Delivery Pick Optimisation and Routing -?Last mile delivery is expensive that necessitates the need for resource and spend optimisation. AI will play a critical role whilst enhancing the customer experience. ?

ZopSmart have enabled retailers to use AI through every step of picking, packing and last mile delivery.

In picking & packing, AI informs:

o???When to allocate orders to staff for picking and to which staff member based on the required delivery time

o???The sequence in which the items should be picked to improve the speed of picking and how items should be separated for packing, and then which vehicles they should be allocated to

The combination of the above has seen as much as a 3x improvement in picking and packing efficiency.

In last mile delivery, AI:

o???Allocates (and re-allocates) orders to vehicles to target 100% utilisation while minimising costs

o???Determines the optimal routes for drivers to take, while considering expected traffic patterns for the given time of day. In the event of unexpected traffic or road closures, the route and delivery sequence can be adjusted

o???Doing all of the above also allows customers to be provided with much narrower delivery windows (e.g. 30 mins rather than 2 to 3 hours). Even in the situations, where unexpected traffic causes a material change, at least the customer is immediately notified of the change, the reason for the change, and the new delivery window.??

AI has helped to drive down last mile delivery costs by as much as 40%.

There are other topics around fraud detection, cyber security, automated code writing that are evolving fast but I have not had the chance to explore in more detail. It is clear that AI will change retail and it is critical for leaders to understand its impact and adapt their strategies and skill base to remain competitive in the marketplace

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About the Author

Rajesh is a strategic executive leader with a proven track record of customer focussed growth and digital innovation. ?He has over 20 years experience in retail holding a variety of senior roles in customer and channel development at Central Co-op, Pets at Home, Sainsbury’s and Argos.?

Rajesh is also a Non-Executive Director for Habinteg – a social housing provider building and promoting accessible homes and communities for disabled people