An Interview with Sabah Lone, Sales Engineer, Connotate
What is the coolest, most interesting data extraction project you’re trying to solve for a client or prospect?
Sabah: I had this client that wanted to be able to find out what their competitors were offering to their customers for used electronics. They pay cash or store credit to their clients for i-devices, like iPhones and iPads, et cetera. Then, they refurbish the electronics and resell them.
To be competitive, they needed a scalable, Web scraping product to find out what their competitors were offering customers for their used electronics. They couldn’t offer/pay $150 for something that their competition would only be paying $100 for – this would eat away at their profits.
What made this such a cool project to you?
Sabah: The problem is the way that the competitive sites work – they want lots of detail on the product they’ll be purchasing for resale. To find out the value of their used electronics, customers have to answer a set of questions about their iPhone or iPad. The answer to each question results in a next question.
For example, if you were selling an iPhone, the first question you could get is, “What condition is the iPhone in? Is it in good condition? Is it cracked?” Then, they ask the next question, “Does it turn on?” These questions are dependent on the last answers, so they don’t all show up on the screen at once. You have to go through various screens to get to what you really want to know – which is that you will be paid, like, a hundred dollars for your device.
Our Web data extraction technology could fill in the series of questions based on every possible combination of answers, resulting in all price variations offered for the used electronics. It’s pretty amazing how our competitive intelligence solution worked.
What was the situation when they came to us?
Sabah: They were doing it manually, and that made it labor intensive and incomplete. They just couldn’t scale the effort to cover all products, of all conditions. It was too time-consuming and they knew that they could no longer sustain what they were doing.
What kind of data were we able to get for them?
Sabah: They wanted all of the possible combinations of questions asked, as well as all of the possible combinations of answers given to get the price that was offered for each. It wasn’t just the price that they were looking for – it was every step that led up to that price. We were able to get them all the different variations of answer-question results based on every combination of condition, age and product based on the choices provided by each of the iterative question-answer scenarios.
Apart from the fact that there were all these question/answer combinations, what else made it challenging?
Sabah: The sites were all different, because the answer-question patterns were different, and so on one website, you could answer a question one way and get a different answer; a different question next, and the same for the other ones.
What efficiencies are we bringing to their process?
We’ve made them more competitive. We’ve sped up the web scraping process and the time it takes to find out how much competitors would pay for used electronics. Which, in turn, made it so that they could increase their margins when it came to resale of these items.
To give it more context – the client could put their staff on a single site for an entire week – filling out as many question/answer scenarios as they could – and they wouldn’t finish a complete product line. Once we determined all possible question and answer scenarios, we could get all of the pricing possibilities for the iPad product line in less than 30 minutes.
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