Collective[i]: How the FAANG companies inspired a B2B sales solution


It’s a sign of the times that it’s almost taken for granted when we hear about applications that integrate AI into their solutions. But when the company was founded back in 2012, Collective[i], short for Collective Intelligence, was one of the first to go that route. At the time when the world fell in love with big data, Internet giants liked it Amazon, Googleand Netflix was perfecting their approaches to using neural networks and other machine learning methods to drive their core businesses to dominance, and this was where the company got its inspiration.

Collective[i] chooses where CRM, sales force automation, and customer journey solutions stop optimizing the last mile: delivering a cloud-based SaaS solution that helps B2B companies with predictive and prescription analytics close sales. CRM tracks customer interactions as captured by internal transaction systems, while customer journey solutions approach the problem from the opposite perspective: what are the paths that companies open for their customers and how do their customers navigate them. Meanwhile, automation of the sales force, traditionally part of CRM, manages workflows for managing sales channels, forecasting, and tracking team performance.

At first glance, the issues that collective[i] addresses are not so unique: how to best predict and close sales. It focuses on workflows from forecasts to prospecting. Collective[i] is not alone here as and Salesforce inbox also capture client activity and like Collective[i], collect email, appointment calendars, invoices, video conferencing, recorded phone calls, external market data, etc.

But there is a huge difference that sets the collective[i] except: While most sales optimization solutions only reflect the client’s internal data, which in some cases can be supplemented with third-party data, Collective (i)’s dataset is far more massive. Collective (i) clients agree to make their customer interaction data available so that they can be analyzed anonymously as part of a master dataset. A form of Crowdsourcing of companies, Collective[i] characterizes this as a network. Its data set is quite wide; the company estimates that in 2019 it alone captured 2% of the world’s B2B data traffic. It is the data set used to generate models that support forecasts and various forms of customer purchase propriety analysis.

Their solution starts with the use of Robotic Process Automation (RPA), which automates the registration of activity that can be traced back to CRM systems; this eliminates or at least greatly reduces the legwork that CRM users have come to hate. It automates forecasting using machine learning to generate probable predictions based on aggregating buying and market data instead of relying on subjective opinion.

Stealing a page from the second-best offer or product purchase recommendations that consumers get from e-commerce sites, Collective[i] then generates recommendations on the second-best actions that salespeople should take. And with that comes collaboration features through “virtual dealrooms” where sales teams can coordinate prospecting and then identify and provide targeted guidance for stopped opportunities. The feature, which is currently compatible with all major CRM providers, logs email and calendar activities and mines communication to email signatures that provide current titles or positions. It is now enhanced with an option to decrypt digital signatures, video conferencing and VoIP calls and adds opt-out and other options for complying with data protection mandates such as GDPR or CCPA.

AI is the key to collective[i]’s solution. It is in itself hardly unique. For example, Salesforce has integrated Einstein in its Sales Cloud, Service Cloud, Marketing Cloud, App Cloud, Analytics Cloud and Community Cloud. In the meantime Oracle and SAP has sprinkled in AI to answer questions like the likelihood of closing a sale, what actions need to be taken to close a sale, what deals are likely to close the fastest, and what prospects need to be prioritized.

But as mentioned collectively[i]The difference is that it goes beyond the framework of a single company’s sales history, and thus captures a better picture of the buyers that are targeted at, e.g. what other suppliers they work with and what other products they have bought or are about to buy. Collective[i] have you built graph neural networks to resolve the identities of individual buyers and business units and have patents on how they are able to unite all the identities under a common data model.

It also applies recurrent neural networks (RNNs) and draw neural networks for the analysis of sales pipelines, predict the probability of tenders closing, and when and at what value. Models are generated based on the corpus of data collected[i] has gathered from its entire customer base and shown what offers did and did not close, and all the mitigating factors around them. The underlying graph represents the strength and nature of interpersonal relationships, while the RNN model is built with time series events with historical reference that can decipher the strength of possibility.

So when you apply AI to a much broader dataset, how does it improve results? The answer is to get a more nuanced picture that can ultimately shed more light on buying propensity. As mentioned above, it starts with the broader database of data from multiple companies where they can train and develop AI models. Then, within a single company, it could train the gaze of seemingly unrelated transactions that could provide signals about the likelihood of an individual sale closing. For example, a company that has just changed its office leasing and reduced the amount of square footage is more likely in the market for new office equipment and potentially cloud computing services. Or a company that uses mainframe software is less likely to buy clusters equipped with advanced GPU processors.

Insight can also capture human dynamics by deepening the behaviors and interrelationships between different purchasing groups in a business. If one group needs the other’s support for the green light of a purchase order, their history of collaboration or lack thereof should provide an indicator to the sales team of how much they should prioritize pursuing the lead.

Collective[i] is not the first company to build analytics based on network data. As mentioned above, it drew its inspiration from FAANG companies that leveraged their broad reach with consumers to build strong advertising and entertainment businesses. The difference here is that Collective (I) applies this approach to the B2B world.