Business tech is still behind, but it’s able to learn lessons from the consumer side.
If you spend any time at all online, you have almost certainly experienced a form of data targeting. You know the drill: you log onto your Amazon account to search for an item you want to buy, and less than ten minutes later, that item and items like it are showing up in the sidebars of other websites you’re browsing. A half hour after that, you’re seeing ads at the top of your personal email inbox for the same merchandise. It’s not a coincidence — it’s the result of targeting technology from consumer sites that can track your searching and purchasing behaviors, actively trying to get to know your preferences in order to better serve you content that you are likely to consume or purchase. If you’re not expecting this sense of “connectivity” between vendors, the experience may seem annoying or even downright creepy depending on the context.
When targeting works well though, it provides an amazing sense of convenience and reduces the overall work required by the consumer — sometimes even driving to a better outcome. Imagine booking airfare for a weekend getaway and then having an offer for a great hotel deal (that you otherwise would not have found) show up in your inbox. Fewer mouse clicks, information that you need at your fingertips, and an optimal outcome. Sounds much like the holy grail of knowledge management to me.
The mechanisms that make this possible are similar in form — albeit, more sophisticated — to some of the technologies we have discussed in this column before. AI tools, specifically machine learning and data analytics, are two common technologies used by marketers and content providers to analyze information and deliver content at the right moments to the right audiences. A wide range of online consumer content, from the product suggestions you get on Amazon to the movie recommendations you see on your Netflix account, are generated by algorithms that have identified commonalities in your search history and preferences.
Many of the technologies that we enjoy today — the Internet, mobile phones, and GPS to name a few — rose out of military investments as part of the Cold War. The trend I think we will increasingly start to see is the consumer marketplace (which seems worlds ahead of the business marketplace) start to influence the business-to-business (B2B) market. In recent years, AI tools have become more common and ingrained in B2B technology — including legal content and solutions. As specific use cases for these technologies become more developed and more widely used, I believe that there are some trends that we’re likely to see in B2B tech moving forward — and with those trends, there are lessons that the B2B community can learn from the consumer tech world as well.
Using Algorithms for Recommended Content
When you conduct a search for a product on Amazon, the site uses an algorithm to learn about you and your customer journey based on the items you search and the keywords you use. This same mechanism could become a tool for technology providers to find recommended content for their users – including attorneys. An attorney managing a $1B deal in the health compliance space might find it useful to have recommended news stories and resources served to her on her dashboard based on her search history. We already know that algorithms can be built to do such a thing — and if applied in a legal tech platform, it could prove to be a useful tool for legal professionals.
If you use streaming sites like Hulu, Spotify or Pandora, you’ve probably come across one or two recommendations that, from your point of view, make absolutely no sense based on your preferences. This is probably because a) the algorithm hasn’t been that well trained yet, or b) your preferences range so widely that it’s difficult for the algorithm to identify recommendations for you accurately. For situations where the behavioral data is not sufficient to get a good “fit” with customer preferences, demographic data can fill the gap. In the legal context, it’s helpful to know who the attorneys / researchers are, what their area of specialty is, and (potentially) who their clients are / what matters they are actively working on. This data, when combined with behavioral data, can help to refine search / recommendation results.
As mentioned above, the tracking ability of consumer sites can be alarming for many people — particularly as vendors get aggressive in collecting additional demographic data for each of us. The nature of many white collar professionals’ work — and especially that of legal professionals — is often very sensitive, and as such, tech providers should be mindful of their obligations to their clients from a privacy standpoint. Providers could look into making auto-generated recommendations something that could be enabled or disabled, based on their individual clients’ comfortability with that kind of feature.
Dean E. Sonderegger is Senior Vice President and General Manager of Wolters Kluwer Legal & Regulatory U.S., a leading provider of information, business intelligence, regulatory and legal workflow solutions. Dean has more than two decades of experience at the cutting edge of technology across industries. He can be reached at Dean.Sonderegger@wolterskluwer.com.
This post was originally published on abovethelaw.com