Businesses are struggling to interpret mountains of data about their customers and what motivates them. It’s nearly impossible to see the forest from the trees when people are focused on their system of record vs. extracting useful insights from all the signals in disparate systems like Salesforce, Marketo and Google Analytics, as well as unstructured sources from the web.
Few companies have the resources or expertise to mine their data and apply analytics that determine which prospects will buy their products, and when. Those that try to tackle the challenge with lead scoring in their marketing automation systems have to manually define rules based on their gut instinct and a small subset of a user’s activity.
And while some companies have a steady stream of inbound leads, others depend on outbound sales and targeted marketing to drive growth. The most common approach is to buy large lists of questionable leads and hope to find a few good prospects, but this requires a lot of time and money.
How is predictive scoring different than traditional lead scoring in marketing automation?
Instead of manually adding points for a given action, our behavioral scoring models use powerful machine learning to mine the full spectrum of activity data inside a company’s marketing automation platform. Sales and marketing teams can then use behavioral scores to predict which prospects will convert in the next three weeks.
How does Infer solve it and are there any best practices associated with implementation?
We produce accurate, statistically proven customer predictions throughout the customer journey, which help companies achieve significant lifts in win rates, lead conversions, average deal sizes and recurring revenues. Our fit models use predictive analytics and advanced machine learning to figure out if someone is a fit to buy a certain product, and our behavioral models determine whether they’re likely to buy soon.
We do this by analyzing key signals – like a company’s business model, technology vendors, relevant job postings, public filings, social presence, website activities, marketing automation data, product usage data, and other attributes. We’ve found that our customers unlock the most value when they use Infer to not only filter and prioritize their leads, but to optimize marketing campaigns, improve outbound sales, create intelligent lead nurturing, design sales service level agreements, etc. One key best practice we’ve seen companies employ is a simple 4X4 fit and behavior score matrix that helps them develop programs around different segments, for example by sending the very best fit, likely-to-buy leads directly to their top reps.
Our Infer Net-New Leads offering provides sales teams with a new source of high quality prospects by partnering with top data providers such as InsideView, and using personalized predictive models to identify a company’s best-fit leads. Marketing teams have often used Infer to score lead lists on their own, but now they can also purchase net-new leads from us directly, leverage our specialized models tailored to score cold contacts, and pay only for the best accounts.
What are Infer’s key differentiators?
We are unique in the predictive space for a couple reasons – first and foremost because of our deep and focused set of insanely intelligent predictive scoring products. Our DNA is made up of a strong engineering culture arising from Google, Microsoft and Yahoo. We are vicious about acquiring data and finding the areas where data science can unlock the most value for B2B sales and marketing.
Infer’s mission is to help companies grow with the power of data science. Our predictive intelligence helps power a number of different applications for sales and marketing:
- Filtering – Instantly identify good leads while filtering out all of the noise (bad leads).
- Prioritization – Prioritize leads in order for Sales to focus on prospects that are demonstrating strong buying signals and are likely to have the greatest revenue impact.
- Net-New Leads – Fuel outbound sales by identifying a company’s best-fit leads that are not currently in your database.
- Nurture – Monitor leads in nurture databases to send prospects back to sales as soon as they re-engage.
- Exec Dashboards – Guide decision making, spot emerging trends, and track how well demand generation is fueling your pipeline.
Because our goal has never been to build a consulting company, we’ve remained laser-focused on model performance and driving impactful, repeatable results for our customers as opposed to relying heavily on services. That’s why we encourage competitive bake-offs and let both our tech and engineering excellence, and model performance do the talking.
Consider why machine learning and predictive analytics can provide top- and- bottom- line value to organizations like yours with the right tools, training, and processes for a range of objectives and use cases.