kentoh - Fotolia
Predictive lead scoring fueled by artificial intelligence is the next-generation CRM tool to make your sales team...
more agile, efficient and competitive. While algorithms to determine lead scores have been hardwired to sales software platforms for years, adding AI's machine learning will help customize those algorithms to a company's own data set and sales rhythms.
It's one of the most important ways enterprises will implement AI in concert with CRM systems. A good predictive scoring strategy can help a company boost its bottom-line revenues by prioritizing its sales teams efforts, messaging and strategy.
A recent IDC survey on the impact of AI on customer management found that 66% of the 1,028 respondents were implementing or considering implementing predictive scoring technologies as part of their sales process. Of the 292 current AI adopters surveyed by IDC, 83% reported that they used or plan to use sales and marketing predictive lead scoring.
Executing a predictive scoring strategy is a complex process that requires ingesting big data from public sources, advertising and marketing partners, and internal CRM and cloud platforms. Organizations must also develop efficient models associated with sales success that can be improved in response to actual sales performance.
A variety of components of AI analytics can improve lead scoring for marketing and opportunity scoring for sales. These underlying capabilities can also help identify upselling and cross-selling opportunities as well. The field is still in early phases, with various pieces available from vendors as customer services and SaaS components. Leading predictive analytics tools and services that integrate with traditional CRM include offerings from vendors like 6sense Insights, BrightTarget (acquired by Sidetrade), EverString, Market Resource Partners, Infer, Lattice Engines, Leadspace, Mintigo and Radius Intelligence.
Parameters of predictive lead scoring
The three key elements involved in using advanced analytics techniques to improve sales success include prioritizing who to engage with, what to engage them on and how to engage with them:
- The who of predictive scoring is about prioritizing who to call first based on the clients' needs and where they are in the buying cycle.
- The what of predictive scoring is about aligning the sales messaging with the clients' needs.
- The how of predictive scoring can help guide a sales strategy more likely to close a sale.
A wide variety of AI-based tools and services can facilitate some or all three of these processes.
The who aspect relies on quantifying the likelihood that a particular lead can be transformed into an opportunity. That requires analyzing data about a prospect's business or a consumer's behavior and building an AI or machine learning model that can score the likelihood of a fit between an enterprise and the client business or consumer.
The what aspect of predictive lead scoring is about quantifying the strength of a match between an enterprise's portfolio of products and services and a prospect's needs. In this case, predictive scoring can help the sales team craft the appropriate sales messaging for correlating an enterprise's offering that will be of the most interest and relevance. It can also help identify cross-selling and upselling opportunities.
AI algorithms for customer segmentation can help improve messaging for classes of customers. Emerging technologies like hyper-segmentation can go a step further by helping to customize messaging for a single large account or individual consumers.
The how requires making sense of a client's buying process to align sales resources with a client. This involves gaining insight into how the client makes purchasing decisions, such as which decision-makers must be involved, how the decision-making process progresses through the organization and the steps required for completing a purchase. In these cases, predictive scoring can help sales reps prioritize their communications with individuals in a company and their relevant needs as part of the sales process.
In the case of B2C businesses, the how can give a sales team insight into the best channels and rhythm of engagement with a prospect for new sales or upselling and Cross-selling opportunities for existing customers.
Crafting a lead scoring infrastructure
A predictive scoring infrastructure depends on using multiple data sources, effective sales models and integrations into sales force automation tools. In the B2B space, enterprises can take advantage of publicly available data about companies, curated business data and internal enterprise CRM and transaction systems. A wide variety of other data is also available about consumers, but it must be managed in a way that respects privacy requirements and preferences.
B2B lead scoring may also take advantage of requests for marketing content such as e-books and white papers, an enterprise's own website traffic and curated third-party advertising data. While much of this data is relatively anonymized, an enterprise's marketing and sales teams can sometimes identify when several inquiries are coming from a particular company and the types of things they're interested in.
The value of predictive lead scoring initiatives will suffer when analytics are done on poor-quality data. For this reason, it's good practice for enterprises to task an individual or team with analyzing their own internal data stores in terms of quality, precision and usefulness. This must be done with an eye toward how this data can be correlated with the who, what and how elements of the sales process relevant to the enterprise.
Developing a good predictive model for correlating this data with the likelihood of sales success can be complex and vary widely across industries. Various predictive scoring vendors have developed their own internal models that have shown benefits. AI vendors are just starting to provide more customization capabilities for enterprises that allow them to tailor and optimize lead scoring.
Many organizations may prefer to outsource this modeling to a third-party lead scoring service. However, this can lead to vendor lock-in and may reduce an enterprise's ability to innovate better models based on its own sales experience.
The adoption of better AI algorithms for predictive lead scoring can certainly help many companies improve sales. But to get the most value, there needs to be collaboration between marketing and sales efforts. That may require organizational change for many enterprises and not just a technology quick fix.
How predictive analytics can help improve the customer journey
The impact of predictive analytics on sales funnel management
How an AI marketing personalization tool prevents over-messaging