Improving lead scoring with better customer data


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You can use customer data to improve your lead scoring and prioritize the leads that are most likely to convert.

Lead scoring has become an increasingly popular method of prioritizing leads to target potentially interested customers better. Lead scoring is especially useful for B2B companies that sell through channels rather than end consumers (e.g., enterprise software sold through resellers). Marketers should know or quickly understand the concepts working the marketing funnel. However, for eCommerce owners, lead scoring can be a bit new concept.


eCommerce Marketers can improve lead scoring by using customer data management tools like a customer data platform (CDP) to email automation software. So basically, it is taking the data you have on the customer and qualifying it based on your characteristics.

CRM linked lead scores

Which leads are hot? Your CDP can help score and prioritize them down to the segment level. Data from Salesforce or other CRM systems like CRMS sugar CRM and Zoho are first unified with other information about your contacts like email automation, survey, and loyalty apps; Machine learning (#ML) uses unified data to make predictions based on predictions behavior and determines which contacts are hot. These leads are then identified in the CRM again, and the new reasoning is supplied to the Sales Direct Representative (SDR) or Account Executives (AE). Sales representatives will appreciate the improvement in lead quality, especially if the information is about what makes the customer tick.

ML also traces the leads to the initial marketing campaign and sales strategies. AB testing is meaningful to find the best strategies. Save money and use those campaigns. This traceability allows you to assess the effectiveness of your lead scoring easily and identify the customer along the customer journey from new acquisition to retention strategies to repurchase.

The takeaway here is that behavioral, demographic, and attitudinal data is being collected in CDPs, so this data must be accessible by sales teams when they are prioritizing leads. Lead scoring has gone well beyond just collecting demographic information on prospects.

Sales can use lead scores. Lead scoring taps into customer data, CRM information, and other sources to predict which leads are most likely to convert. Lead scoring is a way to rank your potential customers and the level of interest in what you’re selling. This ranking system qualifies leads before sending out marketing messages or makes sales outreach more personalized based on how much money each person has made from other products over time.

ML also traces the leads back to initial marketing campaigns and sales strategies when lead scoring accuracy improves sales increase.

Whether it’s a new customer or renewal, effective B2B sales depend on inbound leads. When prospects raise their hands and ask for more information about your product/service, you can rest assured knowing that the ball is rolling towards closing deals with these warm contacts rather than fighting an uphill battle to convince someone unfamiliar with what you have to offer them take up time meeting.

The input tells us why having good connections within one’s organization makes all aspects easier; this includes getting meetings started, eventually leading down a path into larger purchase agreements.

More about Machine Learning Lead scoring

Lead scoring helps create a business case for your product/service based on what you know in real-time about the customer. This type of system is self-learning. It speeds up the process by which sales reps can prioritize customers with better accuracy to focus on leads who are most likely to buy instead of wasting time on contact that may never become customers.

Lead scoring is a great way to ensure that you get the most out of your marketing leads with data-backed evidence. This process gives each contact, in turn, an assigned number that corresponds with one-off our many options, depending on their specific needs and how they may be ranked based on those details.

Contact attributes (e.g., demographic information)

How they come to your site

Specific behavior/actions (engagement with your emails, visits to specific pages on your site, etc.)
Sort through all of the leads which come to your website and identify the most engaged and ready to convert. So, how exactly do you set up a lead scoring model?

Lead scoring is an important process that helps businesses prioritize which leads are the most engaged, qualified, and likely to convert into sales. Lead scoring also makes it possible for marketers to identify bad leads before spending time on them. Lead scoring models may be improved by using customer data management tools like a CDP to make predictions based on behavior and determine which contacts.

Multi-channel lead scoring

So how do you set one up for the more complex multi-channel lead scoring? It’s quite easy to use a marketing automation platform integrated into your CRM; Both platforms integrate with a CDP like VIEWN. This recipe walks you through one of the ways you can up-level (and automate) lead scoring to help your sales team focus on the most important accounts. By important, we don’t just mean the best or biggest companies out there, but the companies most likely to convert into customers for your specific business.

  1. Map out your buyer journey and assign scores based on actions and events
  2. Set up a marketing automation workflow that assigns points to actions taken by a lead
  3. Dynamically segment your lists with lead scoring and discuss personas.
  4. Look at how your business can benefit from an ML-driven lead-scoring model.

Marketing automation is one of the most powerful tools in your marketing toolbox and connects well with a customer data platform. The easiest way is to implement a lead scoring workflow with marketing automation. You get it.

You’re probably already aware that automation allows you to send automated emails, for example, welcome messages, abandoned cart follow-ups, etc. These emails are sent at the perfect time to your customers and combine these with other conditions and filters for more personalized communications.

But did you know you can do much more? Businesses that use marketing automation to speed up their sales funnel see a triple-digit rise in qualified leads. If you’re not getting that kind of boost in leads from your marketing automation, maybe it’s time to step it up a gear.

By understanding your buyer journey and assigning points to different actions and events, you can set up a marketing automation workflow that will dynamically segment your leads and help you discuss personas in more detail. You can also see how a machine learning-driven lead scoring model could benefit your business. Contact us today if you’re interested in learning more about how CRM-integrated CDPs can improve your lead scoring. We’d be happy to show you how our solutions can take your sales process to the next level.

Areeya Lila
Areeya Lila has a passion for customer experience and over 20 years in technology. She's an entrepreneur who loves building products; currently, VIEWN enables eCommerce stores to provide the best possible shopping experience through artificial intelligence (AI) powered data analytics and customer personas.


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