MAM
GUEST COLUMN: A complete guide to retail analytics for 2022
Mumbai: There are a million things that retailers need to do every day. From creating strategies for attracting new customers to finding ways to retain old customers and introducing exciting offers and brand campaigns, they have to also keep an eye on the fierce competition in the market as well.
But, coming up with strategies and implementing marketing tactics is not enough until you track them right!
Data analytics must be a crucial part of every retail business these days. Whether the website traffic, engagement rates, inventory, revenues, or expenses, tracking every retail marketing metric is essential. By monitoring your data the right way, you can gain meaningful insights and make better, informed decisions for your business.
So, let’s dive into what retail analytics is and how it can be leveraged to make your retail business a success.
What is retail analytics?
Retail analytics refers to collecting retail data and analyzing it to gain meaningful insights into the performance of the business. That is information about their stores, vendors, products, and customers. Retail analytics allows retailers to harness all the data, discover trends, and make predictions based on the current data values.
Types of retail analytics
You can tap into different types of retail analytics that can help you understand the performance of your business in a better way. These include:
In-store analytics: This refers to the methods you use to collect data from your retail store. For instance, foot traffic, dwell time, conversion rate, etc.
Web analytics: You must also know how your website is performing. This includes tracking metrics like website traffic, conversions, and sales.
Inventory analytics: Keeping track of your stock is also crucial in retail analytics. It helps you determine which products are doing better and which are not. The primary metrics here include sell-through rate, stock turn, etc.
Customer analytics: It is all about knowing your customers—for example, customer retention rate, churn rate, customer loyalty, etc.
Importance of retail analytics
When you have all the data you need, you can use retail data analytics to improve various aspects of your business. Here are a few points highlighting why retail analytics is crucial for every retail business.
1. Better sales and marketing tactics
Retail analytics can help you understand your customers at a deeper level, making it easier for you to market your products to the right customers and in the right way. For instance, data analytics can help you find out what messaging attracts more customers or which social media channel has the highest engagement rates. This way, you can improve your marketing campaigns accordingly and drive sales.
2. Improved business management
Retail analytics plays a crucial role in enhancing day-to-day business management. It allows you to predict which products are being preferred by customers these days, and you can make decisions on stocking, tracking, and restocking the units accordingly. You can keep track of how a particular product sells and understand the current demand in the market.
3. Enhancing customer loyalty
Retail analytics helps you keep track of purchase history, shopping patterns, preferences, and other essential metrics associated with every customer. As retail analytics enables you to analyse customer behavior better, you can use this information to provide a better, personalised shopping experience to every buyer.
4. Better in-store operations
With in-store retail analytics, you can make changes around your store to attract more customers and increase your sales. For instance, you can determine which store layouts attract the customers the most or which product placement draws maximum attention. You can enhance your staffing, include appealing designs, and implement effective sales tactics to make your offline store a hit.
Data analytics is not as easy as it sounds
Many marketers these days struggle with data analytics. According to Marketing Revolution, 57 per cent of marketers incorrectly interpret data and likely get incorrect results. The main problem that retail marketers face these days is the lack of data which leads to:
Uninformed decisions and underachieved goals
Poorly performing campaigns since marketers have no idea which aspect of their campaigns to improve
Unnecessary investment in data analytics tools and vendors
How to utilise retail analytics for your business?
Retail analytics can help take away the guesswork out of your business. It gives you a reality-check of how your business is performing and enables you to keep track of every aspect of your business, from sales to inventory and customer experience. Here’s how you can overcome data analytics problems and make use of retail analytics better.
1. Integrate various marketing channels
Keeping data from different sources distinctively makes tracking it a difficult task. The first step to retail marketing involves connecting all your marketing channels to a single data platform. Bring data from multiple marketing channels to a centralised place so that you can track data, find patterns and understand your customer journey in a better way.
2. Real-time tracking
Track the critical metrics every single day! Real-time tracking allows you to understand the current market situation. This way, you can take immediate action and see results. You can send the proper communication to your customers at the right time and promote your sales.
3. Represent your data visually
Do not just rely on spreadsheets for retail analytics. Make use of charts, graphs, and funnels to understand every little detail. Visual representation of data will also make it easier for you to identify patterns and understand the performance of your business in a better way.
4. 360-degrees retail analytics
This is considered one of the most important analysis tools for a retail company. It is a compact, easy-to-read, insightful report that combines all the customer metrics in one place. For instance, a 360-degree customer profile helps you understand their buying history, interests, preferences, shopping patterns, and demographics.
5. Access analytics data from anywhere
Data should be available to retailers at all times and across platforms. This means you should be able to access and manage your analytics dashboards at any time from your laptop, tablet, or even mobile phone. This way, you can share this data anytime, from anywhere, with your team and keep track of your retail business performance.
Some tips for better retail analytics:
Here are some essential tips that retailers should keep in mind to ensure a successful data analytics process for their business.
1. Focus on key metrics
There are different key performance indicators (KPIs) in retail marketing. But, not all of them might work for all retail businesses. So, you must find out which metrics affect your business the most and are relevant to you. Track them and make the best use of retail analytics. Some important retail marketing KPIs include:
Customer retention
Average transaction value
Conversion rate
Foot traffic and digital traffic
Sales per square foot
Inventory turnover
Gross margins return on investment
2. Be consistent
Retail analytics should be something that you do regularly—for instance, weekly or even daily. When you track the metrics constantly, you understand the various factors bringing that change in a better way. For example, if you follow your sales weekly, you can quickly determine if your sales are dropping and immediately take action.
3. Connect different metrics
If you want to gain clear insights into the performance of your business, you need to relate the various metrics and analyze them. For example, foot traffic should be associated with the number of sales to determine whether people entering your store are actually buying your product.
4. Collaborate with your team
Clever algorithms and practical tools are essential, but so is a team that can study the results and gives its opinions. Talk to your staff and understand what they are experiencing on the frontline. Then match their experiences with the results from the numbers you have collected. Allow your team members to bring in different perspectives in analysing and interpreting data to create better marketing strategies.
5. Use intelligent tools
Last but not least, find a tool that can help you maximise your retail analytics efforts. Pick up a tool that can help you collect, measure and analyze data all in one place. You should be able to spot long-term trends, track every metric, integrate with other tools or applications, and access data from anywhere you want.
Gain a competitive advantage with retail analytics
There is no denying that data can do wonders for a retail business. But, it is essential to note that data alone cannot do everything. You need the right analytics tools to extract the correct value from the data you have collected.
(About Author: Pranav Ahuja is the co-founder and CEO of Xeno)
Digital
GUEST COLUMN: How AI is restructuring distributor and retailer motivation models
From incentives to intelligence, AI is redefining how brands engage channel partners
MUMBAI: Artificial intelligence is rapidly transforming how brands engage with their most critical yet often overlooked stakeholders: distributors, retailers, and last-mile influencers. For Abhinav Jain, co-founder and CEO of Almonds Ai, this shift marks a fundamental departure from traditional, transaction-led incentive models toward behaviour-driven, data-intelligent ecosystems. In this piece, Jain examines how AI is enabling brands to decode partner motivations, predict engagement patterns, and deliver personalised, scalable experiences—ultimately redefining channel relationships from transactional exchanges to long-term growth partnerships.
Across many sectors, there is increasing recognition that motivating those who bring products to market (distributors, retailers, last-mile influencers) poses a growing challenge.
Brands continue to invest significant marketing and digital resources to consumers, yet in many countries and the vast majority of emerging economies, these types of consumer-focused investment areas have had little impact on ultimate product delivery. Rather, it is still the case that traditional retail continues to make up most products sold.
So why is it that the systems built around motivating these channels have yet to evolve?
For decades, distributor and retailer engagement revolved around static schemes – quarterly targets, volume-based rewards, and occasional trade promotions. These programs were designed around transactions, not behaviour. The assumption was simple: if incentives increase, performance will follow.
Now, with the advent of artificial intelligence, the definition of performance is being challenged.
With the development of artificial intelligence, businesses can move beyond simply creating loyalty based on transactional-based models and toward models built on behaviours, the behaviours of channel partners that are intrinsic to their motivations in engaging with particular brands. As a result, the means by which businesses develop relationships within their distribution network are starting to evolve; thus, ultimately changing how brands interact with those within their distribution network.
Assessing engagement: Transitioning from transactional- to behavioural intelligence
Traditional loyalty systems refer to transactional activity (sales data). Although this data is valuable and important, it only provides a partial view of engagement across the channel partner.
For example, a retailer may have a high frequency of sales of a product, but their lack of engagement with the manufacturer would not reflect that they have true loyalty toward that brand. Conversely, a retailer who actively participates in training programmes, acts as brand advocates, and is engaged in learning with the supplier would exhibit more profound levels of loyalty but would have been invisible based on historical incentive programmes.
Artificial intelligence allows for the identification of behaviours that help to address this gap. Brands are able to use a variety of engagement data points, participate in learning programs, respond to communications, redeem behaviour and track platform use behaviour in order to identify motivation through behaviour.
McKinsey has stated that companies that leverage advanced analytics for their sales and distribution functions can achieve as much as a 15-20 per cent increase in productivity due to increased awareness of their behavioural trends throughout their networks.
This visibility of behavioural patterns within channel ecosystems can be transformational to brands as they can now view how partners engage on their path to purchasing products, instead of just measuring the sales revenue generated by those purchases.
Predicting motivations, not just measuring performance
Possibly, the largest contribution of Artificial Intelligence (AI) to helping brands engage with partners via channel ecosystems is its ability to predict future engagement versus simply measuring past performance.
Traditionally, brands only realised that a partner was disengaged (not likely to purchase products) once their sales performance had already declined. By then, the brand would have to use significant amounts of incentives or aggressive promotional activities to recovery their partner’s engagement level.
AI models can help organisations to detect early signs that a partner is becoming disengaged, such as declining participation in learning modules, declining interaction via the platform, or slower reward redemption rates. These indicators can help organisations to proactively engage with their partners before their sales performance begins to decline.
The practical application of AI and predictive analytics gives brands the ability to re-engage with their partners prior to their sales performance declines. For example, instead of developing and implementing broad-reaching incentive programs that provide a “one size fits all” incentive to all partners in an ecosystem, brands are able to develop targeted, engaging re-engagement programmes. This is how personalisation can be done on a large scale, such as across global distribution and retail networks.
The vast majority of distributor and retailer channels have thousands, if not millions, of individual channel partners. Historically, providing personalisation to such a large number of businesses has not been feasible.
However, with the advent of AI, personalisation at scale is becoming a reality.
Brands can now create tailored engagement journeys for all their partners, based on their partner profiles, through some combination of machine learning models and behavioural segmentation. For example, high-performing distributors might receive higher levels of leadership-based recognition and greater incentives to continue to grow. Emerging retailers, on the other hand, might be supported with training, onboarding rewards, and measurable performance milestones.
The shift towards personalisation of partner engagement echoes the direction that consumer marketing is already moving towards.
According to Salesforce’s report, over 70 per cent of customers expect personalisation in the way that brands engage with them. As such, there is a growing expectation for B2B ecosystems to have these same types of expectations from their channel partners.
Gamification and continuous engagement
AI is also radically changing how brands will engage with their channel partners through the use of gamification.
Many traditional incentive-based contests and leaderboards would spark temporary engagement among their participants, but they struggled to sustain engagement over time. With the use of AI, gamification mechanics are evolving dynamically based on historical and evolving participation patterns by their channel partners.
Challenges, rewards, and recognition structures can be modified continuously in order to sustain engagement with all of a brand’s partner segments. This will provide a greater opportunity to move away from episodic campaigns towards ongoing, continuous engagement experiences.
When channel partners receive motivation as part of their daily business activities through recognition, learning, and tracking their performance, long-term loyalty will be achieved.
Aligning motivation to broader impact
There is a growing trend within the channel ecosystem to integrate sustainability and socially responsible behaviours into the channel partner programmes of brands.
Increasingly, brands are motivating their partners to use sustainable practices in their operations, participate in sustainable practices like sustainability-related knowledge programmes, or promote products that are in line with their sustainability objectives.
Brands can use AI to monitor and measure these types of behaviours and incorporate them into their incentive frameworks so that brands can align their commercial objectives with broader social and environmental outcomes.
A shift in the way brands view their channel partners
AI is having the most significant impact on the way that brands are now viewing their channel partners, as it relates to the underlying philosophy of those fundamental relationships.
For the past several decades, many brands have viewed their channel partners as intermediaries in the supply chain. More and more brands are now beginning to view their channel partners as key ‘partners-in-growth,’ and their actions can have a direct impact on market performance.
In fact, all the channel ecosystems are using behavioural engagement platforms to design new models that reward not just transactional behaviour, but also create continuous engagement journeys for their partners, where their partners can receive recognition for their participation, learning, and continued engagement, thereby reinforcing long-term loyalty to the brand.
The future: Intelligent channel ecosystems
As we consider what the next phase of channel engagement may look like, many believe that it will be based on intelligent ecosystems, using AI to continuously monitor and adjust the engagement strategies used to engage their channel partners, in real time and based on the behaviours of those partners.
For brands operating in complex distribution networks, the ability to perform well will be determined both by whether products are available to their customers, as well as by the enthusiasm, expertise, and loyalty shown from each channel partner that represents the brand each and every day that they are working on behalf of the brand.
While AI clearly does not eliminate the human aspect of a brand’s relationship with its channel partners, it does allow brands to better understand and nurture that relationship.
In markets where the last mile will determine whether a sale is made, how one leverages the intelligence gained by using AI will ultimately be the difference between gaining a new, sustainable competitive advantage versus losing one.
