Machine Learning is being adopted by more and more companies to assist in the sales process. Like all technology, Machine Learning is not correct all of the time. In fact, models with 75% accuracy are commonly accepted as good models and implemented for production. This means 25% of the time the model is wrong, meaning that the algorithm will incorrectly flag 25% of the business. What happens to transactions where an algorithm has incorrectly determined the transaction is not viable? Legitimate business is turned away. This happened to me when I tried to do business with Lenovo. If you are someone who is genuinely interested in how technology has adapted, then you may want to check out this quick guide on some tech tips that might be able to help you with any issues that crop up, unfortunately for me, Lenovo was my issue and I needed their help.
Lenovo’s Ordering System: No Laptop For You!
Buying a laptop from Lenovo reminded me of an episode of Seinfeld when Elaine was trying to buy soup. For some unknown reason, when I placed an order on their website and gave them my money, Lenovo gave me a Cancellation Notice, the email equivalent of “No Soup for you!” After placing an order, about 15 minutes later, I received a cancellation notice. I called customer service. They looked at the order and advised me the system incorrectly cancelled the order. I was told to place the order again as they had resolved the problem. I created a new order, and just like the last time, I received the No Laptop for You cancellation email. I called back. This time I was told that the system thinks I am a fraud. Now I have no laptop and I have been insulted. I asked if the system could be overridden because I was not a fraud. Customer service verified my method of payment and told me that were going to assign a case number to it as that would ensure the transaction would go through, and they would get credit for the order as they were going to place it. Apparently, customer service has some kind of financial incentive for placing sales. That did not work either as, once I again I received the No Laptop for You cancellation email. Not only did I not get a laptop, the person I spoke to also lost out as he was not going to get a credit for the sale. I called back again and this time they told me that they had no idea what was wrong with the system but it had flagged me as a fraud and a case number did not get assigned last time as it was supposed to, which was the reason that that order was canceled, again. They placed the order again and once again I received the No Laptop for You cancellation email. Every attempt at buying a laptop had failed. I had struck out with customer service as had received advice 3 times and every time I got a No Laptop for You cancellation email. At this point I tried getting the situation resolved via social media. Publicly Lenovo said they wanted to help, and sent me one direct message letting me know they would fix the system, and that was the last I ever heard from them. By not sending me another email, the message they sent me instead was No Laptop For You!
Relying on Machine Learning can Cost businesses Sales and leave them wondering about Toilet Seats
I tried to give Lenovo nearly $2000 and they refused to take my money. How many other transactions are they ignoring? Over 500? That does not seem like a terribly high number. Jokes apart, many companies tend to limit the sales of a particular new product. This can be a part of market research where companies tend to use tools like Conjoint Analysis (look for – What is Conjoint Analysis?) or similar tools where they can find out the actual requirements of the consumer as well as a viable price point for it. However, this might not be the case as I don’t remember the customer service person mentioning it. Also, 500 transactions for $2000 a piece is huge. That would mean Lenovo’s sales are needlessly down 1 million dollars because they implemented a system that turns away deals and actively prevents sales despite the best intentions of their employees to close a sale. As a result, Lenovo’s customers may have a negative impression of the company, which could lead to a decrease in laptop and computer sales. In this case, the company’s original and powerful brand image can be revived by implementing a marketing campaign, removing negative web content (with the help of an online reputation management firm), and executing effective branding strategies. Moreover, Lenevo may need to consider the primary cause of this problem — the over-reliance on machine learning.
Blindly relying on the accuracy of a computer program to determine with 100% accuracy whether or not a transaction is viable or not is not just a bad idea but is a bad business decision that can cost millions of dollars in sales. While you may not have been rejected to buy a product, most people I know have seen lists of recommended products on websites which do not reflect things you want to purchase. A friend of mine who was remodeling a bathroom, bought a toilet seat on Amazon. When he logged in again, he continued to see a myriad selection of toilet seat product recommendations for the next six months at the exclusion of other products he might actually want to buy. Apparently, the machine learning algorithm determined that because he bought one toilet seat, he was a Toilet Seat Connoisseur and wanted to decorate his house with a variety of rare and unusual of toilet seats for the next six months.
Combining Machine Learning with People
I create machine learning solutions for clients and provide training sessions to help people learn how to write machine learning models. I understand the process and the steps which are used to create a machine learning experiment. First you gather and clean the data, then train it using a set of algorithms against a set of data, and then you create a model. The problem “Should I cancel this sale” is has two possible answers, yes or no, meaning it is a binary classification for anomaly detection. Never have I created a model which was 100% accurate as that is not possible. I tell clients that is not possible and help them implement solutions to handle conditions when the model is wrong. Machine Learning needs to work in concert with people who have the ability to resolve problems which are flagged by the system, as there is a place for people in all automated systems.
Most normal people would have probably given up after their order was canceled twice, but I persisted as I was amazed that such a big company like Lenovo could continue to be so wrong, and I wanted to prove I was not a fraud. Continued failure to successfully place an order convinced me that I did not want to do business with Lenovo. If a company does not want to resolve an issue where they will receive money, how likely are they to want to resolve a situation which costs them money, such as a warranty claim? Based on my experience, I have no confidence that one could get Lenovo customer service to solve a problem as they do not have the ability, even when they are financially incentivized to do so. Machine Learning and AI may decrease the number of people needed, but when things go wrong people are needed to fix them. When a machine learning model is wrong, and this will happen, the policy should be to permit your customer service people to create successful sales. If instead, your customer service insults and ignores customers when machine learning models go wrong, sales will go down as customers will be going to competitors.
I researched laptops as I was interested in having a lightweight powerful laptop which I could haul through various airports to use at clients and conferences, like Live 360 SQL Server where I will be speaking on December 3. Fortunately there are other companies who have determined they do not need to create some kind of machine learning score to sell a laptop, they just sell laptops to people who go to their website and give them money with no problems. Using the same address and credit card information which Lenovo flagged as fraudulent, I bought my new HP laptop, which I will be happy to demonstrate next time you see me at a conference or class.
Data aficionado et SQL Raconteur