How to Diagnose Sell Through Problems Before They Happen

Why is Data Important

Brands want to sell their products, and in order to be successful, they need to leverage all the data they can find. While much data can be gathered at the end of a transaction about a consumer; from gender and age all the way to household income and marital status, very little data is gathered at the front end when a product isn’t selling.  Often brands are left in the dark not knowing why their product isn’t finding itself into the hands of consumers after months or even years of planning and execution.

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The Problem - Brands without data can’t identify poor sell through before it happens

Poor sell-through can de-motivate a team, from the marketing department all the way through to the in-store trainers.  Months of planning as well as thousands or tens of thousands of dollars gone with seemingly no return on the investment made.  Moreover, poor sales can lead to a lower chance of investment to help turn things around, thus leading to further poor sales in the future and thus the flywheel continues.  But it’s difficult to invest into something when no one can pinpoint where exactly things have gone wrong.  Having access to data that can help diagnose sell-through problems before they happen allows brands to  prevent or correct course before things take a turn for the worse.

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Why data from a digital retail sales enablement platform is key

Imagine a new product is launching.  The product team have spent over a year designing, testing and re-designing the product; the marketing team have gone all out to get the product in front of as many potential customers as possible through a variety of adverts and the retail team have come in early and stayed late preparing for the big day.  The product launches and by the end of the week the sales numbers have come through, they’re below what everyone was expecting.  It’s an uphill battle from here and as the weeks go by, no root cause of the problem is identified, sales continue to flounder, and sales associates turn their attention to new products from competitors. 

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Now imagine a different scenario; the product and marketing team have still gone all out to ensure the product launch is a success but this time, 3 weeks before the launch date the head office team takes a look at data from their digital retail sales enablement platform.  They’re able to see a number of data points including:

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Network Level Data - This data shows brands how their brand and product training content is performing across different retail partners in their network.  They look at the Content Engagement data and see that 70% of their retailers have a completion percentage of under 35% for the recently uploaded product lesson.  The head office team knows that if sales associates aren’t engaging with the product training content the likelihood of poor sales is high.  They send an email to each of the retailers respective head offices asking them to prioritise training on the soon to be launched product for the next few weeks.

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Store level Data - This type of data allows the head office team to dive down deeper and focus on an individual retailer.  In this scenario Retailer A has an average completion rate of 60% on the brand’s latest product lesson.  Upon further investigation, they realise that a handful of retailers hover around the 90% completion mark while others have only completed the lesson at around a 30% mark.  An email is sent to the store managers of the stores who haven’t completed the product lesson at a high percentage asking them to mention the product lesson to the department managers in the morning meeting and make it a focus point for the  sales associates.

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Sales Associate Data - This type of data allows the head office team to identify the sales associates that have completed the training content on the new product.  An email is sent to the store manager to ask them to ensure the sales associates that haven’t  completed the learning complete the content before launch and the brand may choose to send a trainer to that location.

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Feedback Data - This type of data allows the head office team to receive feedback on the product training content directly from the sales associates who have been going through the lesson.  The head office team sees that  there are a large number of comments asking product specification questions that are not covered in the brands training content.  Consequently, they ask the training specialists to upload shorter versions of the video focussing on the key points of the product. As a result, training content completion increases across the board.

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Over the course of the next three weeks before the product launch the head office team takes additional steps to ensure the launch is a success based on the data they received from their digital retail sales enablement platform.

Conclusion

In 1996 Bill Gates said “content is king”, almost 30 years later as the world has changed so has the king.  In this new world data is now the new king.

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“Prevention is better than a cure” holds true not only in medicine but also in retail.  Being able to use data from a digital retail sales enablement tool allows brands to potentially identify and address the possible causes of poor sell-through before it happens.  Brands without this data are left scratching their heads looking only at poor sales data wondering why their product didn’t sell with no real solution to the problem.

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If you are a brand looking to improve your relationship with your key retailers in a meaningful, scalable and cost-effective way Myagi can help.  Click the button below to contact us and learn how we can help.

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