AI in Marketing
Poor lead prioritization can have huge implications for sales and marketing with missed sales goals and demoralized sales teams and ruined collaboration. Poor lead quality drives burnout and high turnover in sales positions leading to extra costs for training and more sales staff on hand to generate the required pipeline. Traditional lead scoring methodologies rely on interest from the prospect to determine a score, but the interest level of the individual does not necessarily provide an indication of their ability to purchase.
AI based machine learning models can score marketing leads using a wider variety of factors and learn from those leads that ultimately became opportunities and those that created revenue. By looking at more information about customer behavior, company size, industry, etc., each lead can be evaluated and scored with sales representatives receiving a ranked list of leads for follow-up. AI can also provide reason codes for each lead so that sales knows the key factors that make the lead valuable. This optimized process insures that sales is working the highest quality leads available which drives faster ramp up from working on consistent leads, quota achievement, less turnover and overall lower sales costs.
AI based machine learning models are ideal for offer optimization. Using AI, marketers can determine which customers are likely to be interested in current offers and only those customers will receive them. With AI models, marketers can use more data on customers including browsing behavior, prior purchases, demographics, household data and more to determine customer interests and intent. AI models can be used to create granular segments of customers using rich data and then to determine the characteristics of products or offers that each group would find most interesting. This can help the marketing team to develop content and offers that would best appeal to each segment which increases conversion rates and dramatically reduces waste in creative content development.
AI based machine learning models can easily segment customers using a much wider variety of data including browsing behavior, prior purchases, demographics, household data from third parties and more. The outcome is much smaller groups of customers with clearly defined attributes that are customer-based including common characteristics, interest and intent. This information allows marketers to develop content and campaigns to target the most valuable or receptive segments for a given message or product. This advanced targeting approach focuses marketing creative resources on the right messages for the most valuable customers which yields higher conversion rates and higher revenues from marketing campaigns and reduced waste.
Selecting the next action to take for each customer is an ideal use case for AI models. AI can be used to find patterns in customer data based on browsing behavior, prior purchases, demographics, household data and more to determine customer interests and intent. One such pattern is the next best action or touchpoint that is likely to produce the desired outcome based on successful outcomes from interactions with other customers combined with the individual’s data. For example, at a certain point in their journey, some customers will prefer an email over a phone call. Knowing this and picking the right way to connect is critical to increasing conversions and increasing revenues.