Saving the Stylist Time. Dappad is ripe for machine learning: @dappad_official #dragonsden #toukertime

It’s not often I watch Dragon’s Den and get a little bit exited. Okay I kind of knew that investment wouldn’t be on the table but the opportunity is. What concerned me was that it’s Erika’s gig, she is the stylist, the brand and that brings it’s own problems as growth happens.

Time is the main metric

Throughput of orders and recommendations takes time. The three boxes a year is very similar to Tesco’s “four Christmas’ a year” concept for Clubcard vouchers.

If you reduce the time and you put more orders through. Doing it on your own is possible but growth can only be taken the point of the number of boxes you can put together in one day.

So if we can find a way to save time we can process more. And there are two key aspects that will make that happen: customer preference data and product attribute data.  If you can marry those two then you are on the way to improving process. I don’t know how Erika is doing it right now, from the pitch it sounded like it was all a manual process. I could be wrong.

Machine Learning Can Help

The main focus here is to get machine learning to automate the selection process for Erika, some form of match making algorithm, the who-gets-what selection that gives a list of preferred items to to box.

The final say is with Erika, not the algorithm, and that’s the important part as the customer is still paying for a personal service so there needs to be involvement. Machine learning aids the process but does not take over.

Measure Everything

Peter Jones main beef was over returns which is a reasonable concern. We know what products are going out (from our theoretical system) and we know that some products are going to come back. This becomes a self learning system, items that worked and items that didn’t are fed back into the system so the recommendations can improve.

Be certain of one thing, you will never have a perfect prediction but you can feed as much data back into the algorithm to ensure that your error rate starts to reduce. Once you are increasing certainty then you are reducing the chance of returns. That starts to increase the value of the customer and therefore increases the bottom line.

The matter of held inventory was also an issue, using an automated recommendation there’s a process that could, over time, minimise the stock holding by Dappad and just be able to order in a just-in-time basis. Automate the recommendation across the user base, order from the suppliers required quantities and then box appropriately.

Summing Up

There’s nothing here that I have presented that’s out of the ordinary nor anything that would worry me as a customer. It’s just taking a look at the supply chain process and seeing what could be improved with a little automation and algorithmic learning.

The questions in my head right now:

  • If you introduced 4 boxes a year instead of three what’s the impact to turnover?
  • Can you use Zara supply chain learning to Dappad and get down to near zero stock?
  • Would the introduction of some form of artificial intelligence or machine learning reduce the returns by 30%? If so what’s the financial uplift?
  • Can you replicate to different bands of customer: low spend, mid spent, luxury markets.

Ultimately all five Dragons passed on Dappad and for once in my life I actually think that Touker Suleyman missed a trick here….. no #toukertime this time.


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