A customer relationship management (CRM) team can help improve a retailer’s Commercial Offers by using customerdata and insights to inform product selection and placement decisions, promotions, pricing, products availability, andCustomer Engagement through personalised campaigns. Here are several ways that a retail CRM team can implementto help improve commercial offerings.
In addition to the regular data analyses routinely performed by the Merchandise Team and the Customer Surveys and Social Media interactions monitored by the Marketing Team, the CRM Team can gain insights into consumer behavior and preferences. This information can be used to identify trends and patterns in customer demand, and to tailor the product assortment to meet the needs of the target market.
The CRM Team will setup routine and planned analyses that will be shared with the Merchandising Team. For example: basket components, categories penetration, categories spending, Housewife basket
Reviewing past promotions can help you determine which ones were successful and which ones were not. This can help you identify what worked well and what didn’t so you can adjust improve the effectiveness of future promotions.
There are several ways of evaluating part promotions:
• Sales volume or value
• Sales volume incremental or value incremental
• Market share evolution
• Basket Impact Number of transactions
Because each promotion can have its own business objectives, marketers will choose or the other – or several assessment’s metrics.
The most important is that in the select channel, category, brand or store(s), the habit of evaluating past promotions becomes routed in the promotion planning process.
In addition to an exciting mechanics, diversity in promotions play an important part in generating traffic. When either selecting a future promotion item or collaborating with suppliers to find the next excitement to be brought to shoppers, a quick access to the promotion history of the selected product will immediately inform is it has already been promoted, when an where. Over promoting a similar item doesn’t push sales!
At the same time, it becomes easy to identify if a product has never been promoted.
Marketers can use different approaches to look for their promotion history, be it by channel, categories, sub-categories, or Brands.
In the visual chart above, marketer can easily see where and when a specific product has been already promoted, how successful it was (market share and quantity differential) and at what selling price was the highest volume achieved.
Mistakes in a promotion can have different shape of forms: wrong product selection, inadequate pricing, ineffective promotion mechanics, weak distribution, insufficient availability.
As an example, the transaction above is the result of a specific inventory history query for selected promotion items.
By reviewing past promotions, you can identify any mistakes or issues that may have occurred and use that knowledge to avoid making the same mistakes in the future.
A simple checklist attached to each selected promotion can not only serve marketers to validate their choice, but also help upper management have a quick and global view on how the promotion events will look like.
Reviewing past promotions can help you identify your own strengths and weaknesses when it comes to promotion and campaigns. This can help you focus on areas where you are already good at, and work on improving areas where the Team may have struggled in the past.
In the visual representation above, marketers can easily identify not only the sales value of the promotion (size of the bubble), but also how successful it was in terms of traffic and spending.
A promotion strategy is often defined at Category Level, channel by channel and can address several objectives: traffic building, in-category volume driving, cross-categories discoveries, market share gain, …
In all cases, a reference to the past promotion calendar provides, from a strategic perspective, important insights on how the strategy must be rolled out and planned:
• By Channel
• By Brand
• By Category
All Machine Learning (ML) refer to the past to predict the future. Hypertrade’s Promotion module does the same: we use past sales data and a number of promotion-specific parameters (calendar, promotion mechanics, selling price, shoppers’ profile, sales trends, store distribution…) to calculate the projected sales of a promotion item.
ML forecasting is the latest filter that we finally tell if the promotion selection is attractive to shoppers or not. In the example below, the forecasts shows that the promotion should sell 7 units more per day than usual, or a 15% sales increase
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Hypertrade is a retail tech company in Category Management and Shopper Behaviors Analytics. Manufacturers use our platform to manage their multi-retailer data sets and to automate their category management and sales analytics. Retailers use our platform to improve their Commercial offering, collaborate with their suppliers and Engage their Shoppers. Building on Sales, POS & Loyalty data, some of the things we fully automate are:
• Brand& Category Score card
• Brand automatic Diagnostics
• Range Optimization
• Promotion Planning & Forecasting
• Shoppers baskets analysis