The webinar was presented by Chris Barber, a leading Space, Range and Display (SRD) expert and Sid Sarangi, the brain behind Retailigence, a technology startup redefining SRD using Artificial Intelligence (AI) and Machine Learning (ML).

The webinar touched upon the following points:

• Evolution of SRD in retail
• How ML can help simplify complex SRD processes
• How ML actually replaces the human intelligence element
• Deployment and how critical it to success
• Advantages of how SRD being “Customer pull based” rather than a “Supply chain push”

How did it all start?

Whereas SRD is considered as a relatively new capability by retailers, it is a distinct activity that retailers have been doing to promote their products in stores for a very long time.

Chris recalled growing up in a small village, where there was only a small store in the middle of the village. The store owner knew every single customer who walked in, their likes, their preferences, their shopping times and their buying patterns. His shop represented a personalised, localised experience to whom his customers were loyal.

This is the first seed of SRD which created the concept of a highly personalized independent store and it started to scale up with the opening of more independent stores.

Then came the era of Department Stores – in these bigger stores SRD developed into a model depending on the historic sales pattern of customers, which increased profit, broadened the space and increased the range.

Later when Department stores became bigger supermarkets or specialist retailers, the concept of Personalisation was sometimes lost because the SRD design was centrally controlled. These were the early days of Planograms where the priorities of the SRD were often heavily “Supplier-Driven”.

Supermarkets nowadays are increasingly realising that the customer buying patterns, product range and local requirements will be the key factors for success that need to be included in the design of Planograms and SRD overlaying the centrally controlled supply factors.

So, what are the best practices for SRD?

• Identification of space and fitting the range in that space is a model some retailers use
• Identifying ranges to suit the customer profile and fitting it in the space is a model used by other retailers.
• Feedback sessions with stores on space and range are invaluable prior to finalising displays.
• Accommodating the seasonal/festival/locale trends clusters is a key factor which will help create some efficient SRD plans.
• When SRD plans are implemented properly by the stores, it ends up as an enabler which increases profits, reduces waste, increases availability of items and in turn improves the efficiency of the operations. Then, the focus of retailers could be increased on service, availability, and stock management of the items.
• SRD should move towards the “Customer-Driven” approach rather than “Supplier-Driven” approach.
• It is essential that all the teams work together to create a better planogram based on the “Customer-Driven” strategy on giving what customers want and when they need which will improve customer satisfaction. This in turn helps the stores improve operational efficiency and make better availability for the most sold items.
• Displays which are “Supplier-Driven” may mean that the best selling items may not receive the most space.
• “Customer-driven” displays mean that customers easily find what they are looking for, and the products receive the space that they deserve.

How about retailers who don’t know about their space?

It is becoming a common trend among the retailers who are unaware of their own store’s SRD – This is because they think of SRD as a “Mysterious Dark Art” and are genuinely afraid to address the problem. Instead, retailers need to try to go back to the basics of why they are running a store and understand their customers so that they can utilise the space and ranges available for them to increase their profits.

So, what is the Future?

Retail shopping has come full circle – where the current and future customers expect a wide range of personalisation and hyper local experience just like an independent store owner offered in a small village. Tailoring the offers for local customers to suit their needs should be the highest priority for the retailers to achieve greater success.

How can ML help in all this?

Sid Sarangi came in at this point and highlighted how the best practices defined by Chris can be achieved effectively with the help of Machine Learning.

In the SRD Design – in all steps, Retailigence’s ML driven SRD products can play an effective role in achieving the intended goals.

1. Set Space – Create Floor Plans which are more efficient.
Retailigence’s MACROSPACE can do the following for you:

• We can set goals to maximise the space by price/sales/margins
• Assimilate the best performance across the stores depending upon customer demographics/behaviour and decide the space accordingly

2. Agree on Ranges and Assortments
Retailigence’s CLUSTERING and ASSORTMENT OPTIMIZATION products helps making the following tasks easier

• Creating Store clusters/assortments and maximising the space for better profits depending on the customers buying patterns
• Traditionally, the standard parameters which determined the range and assortment (such as Small/medium/Large) and (Affluent, Ethnic, Standard) could be taken to a different level with more data driven segmentation of customers and stores

3. Build Planograms

• Building a planogram should be driven by Customer shopping missions and should be aiding customers and not deterring them.
• It should be more intuitive for the customers – for e.g, if a customer comes in for a garden strimmer, he/she should be able to see the different garden strimmers in one place so that he/she can evaluate the pros and cons of what he is buying. However if all the garden power tools are simply grouped by brand (easier for the retailers) the customer is lost and might walk out of the store without buying anything.
• So, ML can help in creating a better planogram depending on the customer shopping missions.

4. Display Implementation

• Retaligence’s CDT and SHOPPING MISSION product helps create a display which creates a customer shopping mission with greater awareness
• In addition to that, it could be used to monitor range performance, highlight both ranging and operational issues, drive changes in range with the available data and constantly optimising the intended goals

5. Historical Data Analysis and Inclusion

• Retailigience’s CONTROL TOWER does help include historical data when creating all the plans and also helps provide balance while using the data without causing huge disruptions for customers/staff/suppliers etc and also helps understand the benefits.

So, what is the takeaway from this webinar?

Current day customers are moving “Back to the Future” where they expect every single store (big or small), off-line or online to be like the “Store owner” of that independent store in a village who knows everything about their customers.

So, ML driven SRD is changing the focus of retailers from a “Supply-Push” approach to “Customer-Pull” approach.

Catering to the hyper-local, highly sophisticated personalisation tuned to the different customers is the key differentiator for the success of any store and ML driven SRD helps the stores to achieve that goal at its maximum potential.

If you’d like a copy of the webinar recording or wish to find out more about the Retailigence solutions suite, or just speak to the Retailigence leadership, feel free to send an email to richard.harris@retailigence.com

The post SRD (Space Range and Display) – Past and Future appeared first on Retailigence.

We are hosting a series of webinars by senior retail execs on best practices and strategies to make retail stores an attractive proposition again for customers. We will also cover how machine learning is critical to help deliver that strategy.

Here is the recording of our first webinar of the series:

The post Getting customers back to stores post COVID appeared first on Retailigence.

How does a customer see your offering?

For decades, retailers have been internal or inward looking while determining their

• Categories of products
• The assortments and offers that they present to their customers
• The promotions they wish to offer within those categories

The products are categorised how they are purchased from their vendors, how they are stored and transported, and how they are managed within the company.

Let us take a grocery example. A grocer would normally have separate departments for poultry and fish. These departments are managed by different people in the business (The poultry and the fish buyers for example) and sourced from different vendors / farmers. They are negotiated, bought, transported, promoted and sold as say, poultry or fish.

However, a customer buying a ready meal, or looking for barbecue ready product, may select from a set of barbecue ready products (fish or meat) or a set of ready cooked meals (Again fish or meat). For the customer, the categories are ‘Ready Meals’ or ‘Barbecue Ready’. A customer isn’t going to switch from a ready to eat fish cutlet to a ‘ready to oven bake’ salmon, regardless of any promotions that are on offer.

Similarly TVs and Soundbars are traditionally different categories, but customers would normally seek to create a bundle purchase. Unless they are planned and promoted together, this customer offer cannot be created.

Traditional ERP and Planning systems continue to follow these prescriptive and prejudiced models. Indeed in some cases, retailers themselves decide what a customer decision tree must be for certain categories. So much for listening to the customer, putting the customer first etc. etc.

RETAILIGENCE cuts through existing prejudiced rule-based decision making. Using transaction data, we determine the correct product and store affinities to tailor assortments and offers to customer groups in different stores. Machine Learning continually evolves this Assortment 360 suite is one of Retailigence’s offerings which brings multiple solutions in a fully integrated Machine Learning suite which will help:

• Look at products and stores from the customer lens to Create store and product affinities
• Assort sharper and more effective mixes to maximise customer value
• Cut down the long tail of products and shift them to digital or vendor served assortment
• Optimise the space allocation and store flow to different categories based on customer shopping missions using syntactics based machine learning
• Design and deploy cross sell and upsell opportunities
• Monitor the effectiveness of the assortment and identify ranging and operational issues constantly

As a result, our clients benefit from:

• Sharper Assortments. Our clients found opportunities to reduce range by over 20% in certain cases even while increasing sales by 4-5%
• Upsell and Cross Sell recommendations customised to product, location and customer
• Reduced and focused promotions and markdowns
• Deeper definition and serving of customer missions in the store
• An effective omnichannel assortment and reduced stock levels
• A more engaging (as defined by data) store design.
• Freeing space from categories (while increasing sales) and introduction of services within stores (to make them more enjoyable) like cafes, children’s play areas, wine tasting areas etc.

The post Disrupting prescriptive and prejudiced models to enhance customers’ omnichannel experience appeared first on Retailigence.

Owning physical stores is incredibly difficult right now. Retailers are closing or downsizing stores and moving to more convenience formats. However, research has found it’s expected that 78% of purchases will still be made in stores by 2024. It makes it even more relevant that the revised store estate serves the best value by serving a reduced, more attractive range.

Enhancing the sales and uplift, it also helps to provide an attractive shopping window to their store and online channels, quite literally.

RETAILIGENCE has customer use cases where we have managed to demonstrate increased sales and margins even after cutting range by over 25%. Using retail data, we determine the correct product and store affinities to tailor assortments and offers to customer groups in different stores. Machine Learning continually evolves and improves this offer. Some of our algorithms

• Look at products and stores from the customer lens to Create store and product affinities
• Assort sharper and more effective mixes to maximise customer value by cutting down the long tail of products and shift them to digital or vendor served assortment
• Optimise the space allocation and store flow to different categories based on customer shopping missions using syntactics based machine learning

This leads to

• A more engaging (as defined by data) store design
• Freeing space from categories (while increasing sales) and introduction of services within stores (to make them more enjoyable) like cafes, children’s play areas, wine tasting areas etc.

With your data, we can create a proof of concept in 2 weeks for you to review. Send us an email at admin@retailigence.com

The post Cutting through existing prejudiced rules-based decision making appeared first on Retailigence.

RETAILIGENCE is a disruptive startup which uses machine learning to deliver re-invented versions of traditionally rule based retail solutions. Powered by the RETAILIGENCE ML CUBE, our robust suite of solutions help retailers make informed decisions about how they want to cluster stores and optimise assortments. In turn, this enables them to deliver curated promotions aimed at the right customer groups.

The post Reinventing Retail Solutions through AI appeared first on Retailigence.