Ask any company that hasn’t formally invested in AI if they have valuable data on customer behavior, and they’ll likely say no chance. The thing is, however, many companies don’t realize they’re collecting this data through everyday processes — which in turn, can be used to develop powerful AI tools.
Customer emails can provide a huge wealth of data; for example, they can be examined to extract information on how the customer’s ‘mood’ changes over time. Invoicing systems can provide great data, too. This year, for instance, Canadian accounting software company Freshbooks measured how polite citizens are by extracting data from business invoices. So can CRM notes, customer survey responses, meeting minutes, chat transcripts, and more.
Now, imagine being able to measure 10 years of email and invoice history, for example, together. A company could combine the data to predict what actions a customer might take once they start communicating a certain way. If the AI predicts a customer is not going to pay, for example, it would enable the company to react quickly — and maybe even save the contract.
So how can a company find out whether their once overlooked data is treasure or mere fool’s gold? Find a vendor. A vendor will consult companies on their current data structure processes, and explore ways in which the data can be extracted — think extracting data from text, for example. They’ll also present the data to a company in a digestible way, through modern infrastructures like Hadoop, Hortonworks, Tableau or Power BI.
A good vendor will have experience working with a range of client domains, as this diversity enables them to learn quickly. However being specialized in certain vertical is also valuable, so try to find a vendor which clearly markets what they’re best at. And finally, a good vendor should be able to offer packaged data science solutions — such as pre-built models, frameworks, and reusable components from previous projects — to speed up the entire of process of ‘mining for data.’