The first generation of predictive sales is past us. How do we know? As the famous Carl Sagan once put it, “You have to know the past to understand the present”. If there is anything about the ever-altering lead generation industry that’s constant, it’s the fact that the customers are never static and content with their needs and wants. But, then again, why would they be? We currently live in an era where there’s a constant free-flow of information. And in all that melee, the true winner is real-time information. From the perspective of the customer, wouldn’t you want to get information as it happens? Not really. Despite the abundance of real-time information, what customers actually need is relevant information. Having understood that context and well-timed information is imperative to gaining quality leads, the actual riddle is to figure out exactly how many leads can be possibly generated. The lead generation universe is currently leapfrogging from an era of ‘fast-food abundance’ to one of a ‘fine-dining experience’. Fasten your seatbelts. Let’s take a short ride through the history of lead generation. The first known ‘leads’ were generated through word-of-mouth marketing (Think Roman Empire). As the medium of marketing grew, it led to an explosion in the ‘lead generation’ industry. The vanguard of the data-backed lead generation era was a mom-and-pop credit-reporting company based out of a small town in present New Jersey, USA. That company is what is now known as Dun & Bradstreet. They were instrumental in the proper enumeration of data through a compilation of various sources, a lot of which was a huge manual operation. Dun & Bradstreet were the masters of the data universe. Who is important?, what do we know about them today?, how did their stock perform?, what did they reveal in their SEC filings? These are just some of the many questions that D&B could answer for you. But, it was not enough. Despite having pre-empted the Information Age, they failed to notice the biggest piece of the puzzle. The contextual relevance of the information they gathered and collated. The context available from D&B data was outdated, to say the least. This was the ‘Suits’ era. Then came the JigSaw era – while hoovers ‘solved’ the context problem, the long tail data about companies were missing. So, to fill that vacuum came Jigsaw which said ‘Give one contact of yours to get one’. The world of lead hungry sales reps obliged and years later, in 2010, Salesforce came in and swooped JigSaw for $142M. Yes, the Data.com that you don’t like much is actually Jigsaw. But, Jigsaw had a problem. People fed the contact data they had and it wasn’t verified for accuracy and hence not updated. Power corrupts, Absolute power corrupts absolutely. As it became easier to game the system, the quality of the data took a downward spiral. While the quality wasn’t as good as research-based companies like Hoovers, Jigsaw showed that there is a hunger for mapping the business graph. Inevitably, LinkedIn had to be built. This was Web 2.0. The era of user-generated content. Instead of the crowd talking about others, the crowd started publishing about themselves and when they did, they also raised their collective hands to be hired or sold to. Or at least that’s what LinkedIn hoped for. But, for most parts, LinkedIn stayed as a strong source of online bios and a weak source of ‘who knows whom’ and a terrible source of ‘buying intent’. Intent – the holy grail of sales and how many have attempted to decipher it! The intent of people is hard to uncover compared to the intent of companies. Companies leave a trail and often reveal a pattern about how they make decisions at different points in their journey. So, discovering organizational intent is relatively easier than discovering the intent of a person/customer. The era of predictive is upon us (or is it?)Different companies claim to be good at discovering intent. There are companies like Discover.org or RainKing that deliver intent information by brute force – surveys, piecing together job information etc. But, they had to do it for a niche (tech spend within large corporations, for example) because human-powered intelligence does not scale. Predictive companies claim to solve this pain-point by piecing together the information an algorithm learns about your products and how your sales pipeline navigates through to closure over a period of time. If you close the deal on a certain type of customers a lot, it just means that you’d more likely close more of the same type in the future too! Powerful indeed, as long as the ‘lookalike’ modeling that drives the predictions has rich enough data to decide which companies belong to a cluster and again, rich enough data about the universe of prospects, to be able to match them both. Are we connecting as many dots as we should be? That’s the question some second-generation predictive lead generation companies have started answering. More on ‘connecting the dots’ in the next article.
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