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Lead Scoring is a methodology used by sales and marketing to rank prospects against a scale that represents the perceived value each lead represents to the organization. Having a good lead scoring system is the best way to identify promising leads.
It is essentialin running successful demand generation programmes and maintaining sales and marketing alignment. A good predictive lead scoring strategy can help you increase the bottom line by prioritizing sales efforts, messaging and strategy. However,implementing a predictive scoring strategy is easier said than done.
It is a complicated process that entails ingesting data from public sources, advertising and marketing partners, and internal CRM and cloud platforms. You need to develop efficient models associated with sales success that can be improved in response to actual sales performance. This is where AI can play a major role. AI helps improve lead scoring for marketing and sales by identifying up-selling and cross-selling opportunities– it takes the guess work out of selling.
There are three key aspects to a good lead scoring system, this includes identifying who to engage, what to engage them on and how to engage with them:
The who aspect of lead scoring can be achieved by identifying who to call first based on the prospects’ requirements and where they are in the buying cycle. The process entirely depends on quantifying the likelihood that a particular lead can be transformed into an opportunity.It requires analysing data about a prospect’s business or a customer’s behaviour and building a model that can score the likelihood of a fit between an enterprise and the client business or consumer.
The what aspect of lead scoring can be achieved by aligning the sales messaging with the prospects’ requirements. It involves quantifying the strength of a match between a company’s portfolio of products and services and a prospect’s needs. This can helpyour sales team craft appropriate messaging correlating the company’s offering that will be of the most interest and relevance. It can also help identify cross-selling and upselling opportunities.
The how aspect of lead scoring helps identify a strategy that will close the sale. It 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.
While most sales and marketing teams make use of marketing automation platforms to automate their lead scoring, it’s important to distinguish the different lead scoring methodologies companies use to score leads.
Traditionally B2B marketers used rule-based lead scoring methodology to rank leads in order to determine their sales-readiness. Here the scoring and ranking is set manually based on a set of rules. For example: if a prospect interacts with more than 2 emails and requests a download within a month then score them as a ‘Hot Lead’.
Such rules need to be defined in advance. Predictive or algorithmic lead scoring takes a much broader set of data and then uses a machine to learn what activity influenced the leads that actually closed. It then uses this data to predict the best score for new lead activity, making this process of scoring leads is far more accurate and detailed
Adding AI to lead scoring has the potential to help firms increase sales by aligning sales messaging and resources with clients and better prioritizing prospects. AI fuelled lead scoring can be the next-generation CRM tool that makes your sales team more agile, efficient and competitive.
You can leverage the power of predictive analytics and big data by adding the power of AI to your lead scoring, this will help you find paying customers faster like Harley-Davison New York did. Harley-Davidson was able to increase its New York sales leads by 2,930%;it went from getting one qualified lead per day to 40 using AI.
Harley integrated AI with its existing customer relationship management (CRM) system to analyse existing customer data and identify characteristics and behaviours of high-value past customers. For the company,AI helped evaluate what was working across digital channels and what wasn’t and used what it learned to create more opportunities for conversion.
And allocated resources only to what had been proven to work, there by increasing digital marketing ROI. Eliminating guesswork, gathering and analysing enormous volumes of data, and optimally leveraging the resulting insights is the AI advantage.
The adoption of better AI algorithms for lead scoring can certainly help organizations improve sales.As new forms predictive analytics – prescriptive analytics – gain momentum, AI systems like Harley’s are expected toonlyget better at predicting leads and outcomes.Prescriptive analytics, unlike predictive, provides insights on the recommended action that could be taken.
What further differentiates prescriptive is, it not only provides information on what and when it may happen but also why it may happen.Prescriptive models are built by ingesting both structured and unstructured data (text, images, documents, videos, etc.) – this level of information, allows organizations to take a truly scientific approach to marketing, leading to more effective outreach and higher conversion rates. It also mitigates any possible future risk.
But ultimately for companies to get the most value from AI powered lead scoring, there needs to be collaboration between marketing and sales departments. These teams should work together to provide the proper training, messaging and context around lead scores, so that the sales team knows how to properly engage.
By adding in context as to why a lead is likely to convert, your teams will have fewer questions about the quality of leads and instead shift efforts to focusing on closing business.
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