Marketing today has some tough challenges. Even though people in marketing are good at getting potential customers interested (which is a super important goal), they’re having a hard time turning these interested people into actual buyers. Many companies are unhappy with how many potential customers become real customers. Many say turning these interested people into customers is the hardest part.
This might be because the people who are supposed to sell things don’t trust the leads they get from marketing. They might feel this way because they’ve had bad experiences before, like reaching out to someone who was supposed to be interested but then finding out the info was wrong or the person didn’t want to buy anything. So, they don’t want to waste time on leads that might not go anywhere.
That’s why it’s common for sales and marketing teams not to work together perfectly. The big problem is that these business-to-business workers say not having the correct data about who might buy their products is a massive roadblock in getting marketing and sales to work together. So, it’s essential to have good, detailed information about these potential customers that both the sales and marketing teams can trust. This way, they can work together better and hopefully turn more interested people into actual customers, which means more sales and money for the company.
At DataLit, employees encounter many obstacles similar to those other companies face. Even though they were good at finding potential customers, turning those leads into actual sales was just average, matching what others in the industry were doing. They believed they could improve. Their marketing department was searching for more innovative ways to use their data to spot and check the quality of potential customers. This way, they could sort out the best ones and give them to their sales team. This would help their sales team spend less time on leads that probably wouldn’t buy anything. By getting better at figuring out which leads were most likely to buy, they wanted to build a strong partnership between their marketing and sales teams.
In summary, they deal with the same issues that any marketing team does, and they’ve discovered that they can solve many of them by using innovative technology like AI.
Here are some questions that might help demonstrate how to use AI in marketing:
What tasks take up most of a marketing team’s time? How can AI help a team do these tasks better?
How can using AI improve marketing and check the quality of leads?
How can a marketing team understand that AI is there to help them do better, not to take away their jobs?
How will a marketing team have a say in how AI is used? How is a team encouraged to develop new ways to use AI?
What changes need to happen to make AI a regular part of a business? How will AI fit into what an organization is doing or change things to get the most out of AI?
How can teams work better together, like sales, using AI to make processes better or smoother?
Who on a marketing team can help plan and start using AI? What other teams, like sales or IT, will marketing need to work with? Do employees have the right know-how to use AI, or does the company need special skills?
What needs to be done to the data before the company can use AI in marketing? What marketing steps need to change to keep the data sound in the future?
What changes should be managed to make sure the marketing team gets and likes the new AI processes? What problems might happen when the team uses AI, and how can they be prevented?
The ability to use AI depends on how familiar the company is with AI. Have the company used digital platforms that focus on data before? Does the company do most things digitally?