Intellimize, a personalization and conversion provider, is designed to help marketers drive conversions and revenue growth by using artificial intelligence to personalize websites to deliver the right experience to each visitor on any device. Companies rely on Intellimize to help them deliver more revenue, faster and with less work than traditional A/B testing or rules-based personalization.
Integrated into Tealium AudienceStream, a data-first customer data platform that is designed to create highly granular, unified customer profiles from every touchpoint, Tealium Predict aims to create and activate trustworthy machine learning insights using cross-channel customer behavior data. Visitors are scored based on the likelihood to complete any action, and then companies can use this score to trigger action in integrated systems across the entire tech stack. Customers can feed machine learning models with pre-cleansed data, use that data to score users demonstrating behavior with predictive potential and take action on that predictive potential all within the same, natively integrated platform.
Traditionally, CRM solutions have been unreliable for sales forecasting. Companies have dealt with inaccuracies and blamed their bad data. However, things are starting to change thanks to artificial intelligence (AI), machine learning (ML) and predictive analytics. According to Salesforce, only a quarter of companies use predictive analytics. Of those using it, 86% have already seen a positive return. Predictive analytics can be brought to the CRM to create more accurate sales forecasts and provide actionable insights for reps, partners and managers.
Salesforce has unveiled its Einstein Search feature, designed to solve the complexities of CRM search by making them more intelligent, less overwhelming and more personal and effective for sales and marketing teams.
Evergage’s new personalization algorithm, Contextual Bandit, is designed to leverage artificial intelligence (AI) to process vast stores of data, positioning companies to automatically deliver the most relevant and optimal content, offer or promotion to each website visitor, application user or email recipient.
B2C companies use machine learning for a wide range of use cases ranging from personalized recommendations, intelligent chatbots and hyperlocal advertising, just to name a few. While the number of B2C companies using machine learning is skyrocketing, adoption by B2B companies has yet to take off.
It’s time for B2B companies to get their game on. Here are some of the ways machine learning can make a significant difference for B2B companies.
RollWorks, an ABM platform and division of AdRoll Group, has launched a new account scoring feature designed to use predictive machine learning to rank accounts within a target list based on firmographic and technographic characteristics.
Adobe unveiled Technology Previews, which are designed to use AI and machine learning technology to help users test out new analytics innovations in real time.
Three questions encompass everything a c-suite or board wants to know when it checks in with its marketing organization:
Sounds simple, right? You might be shaking your head right now, having full knowledge of the scramble and stress associated with coming to that table with clear-cut answers to those questions.
Artificial intelligence (AI) and machine learning (ML) have long been on marketers’ radars, but it’s only recently that they’ve begun to show up in customer relationship management tools. AI and ML are often viewed with skepticism, but many B2B marketers will discover that these features can enhance their effectiveness in contacting the right leads, closing deals and nurturing client relationships. The underlying fear has been that machines will replace marketers, but the reality is much more promising.
