Artificial intelligence for marketing
Many B2C and B2B companies gather leads with the goal to convert them into customers. We can use your company’s lead data to create an artificial intelligence that will help you increase the quality and value of the leads you attract. Furthermore, the artificial intelligence we build for you will help you save costs and increase your marketing ROI by improving your lead nurturing and lead management.
Questions that will be answered in this post:
- What is the quality of my leads? Which leads can already be handed over to the sales team and which need more nurturing first?
- When is the right moment to make my leads an offer?
- How can I increase the value of my leads?
- How can I attract more leads that actually convert?
- How much time and resources should I spend on each individual lead?
What is the quality of my leads? Which leads can already be handed over to the sales team and which need more nurturing first?
We can use the data of your leads to build an artificial intelligence customized to your business to calculate their conversion probability. We will then use this AI to score your leads according to their conversion probability and send each lead’s score directly into your CRM. This enables you to transfer only qualified leads to your sales team. Furthermore, this will improve the efficiency of the lead conversion process and reduce your costs. In case you have no sales team or your sales team is already working at capacity, your marketing team can use this score to trigger automated communications from your marketing automation tool.
When is the right moment to make my leads an offer?
If you use the AI powered real-time lead scoring system that we can build for you, you will be able to trigger messages to your leads in just the right moment. As soon as the score of a lead surpasses a threshold you can automatically trigger an email, a sms or a push notification with an offer when it is relevant and close the sale.
Use Case: Artificial intelligence for lead management
A day in the life (before)
Scene or situation:
Julia B. is the Head of Sales and Marketing of a company selling renewable energy solutions such as photovoltaic, heat pumps and combined heat and power plants to B2B customers. As the information demand for their solutions increased strongly during the last year, their various lead acquisition efforts and the lead forms on their website have created more leads than the sales team can handle. Julia tasks her marketing team to group the leads into high quality and low quality leads. She wants them to send only high quality leads to the sales team and build a lead nurturing journey for the low quality leads.
Desired outcome:
Find and label the leads by their conversion probability. Send the leads with the highest conversion probability to the sales team until their capacity is full. Send all other leads into an automated lead nurturing journey.
Attempted approach:
Julia’s marketing team brainstorms on how to separate high and low quality leads. They order an analysis on the leads that converted to customers in the last year from the analytics team. After a few weeks they receive the report. The report shows some relationships between a high conversion rate and characteristics of the leads. One graph shows the conversion rates per acquisition channel. The acquisition channel with the highest conversion rate is organic search. Another graph shows that leads who have read more than three articles on the blog, have a higher conversion rate than leads who have read less. A third graph shows that depending on the country of origin, the conversion rate varies strongly. As they need to make a decision fast, because the sales team is under pressure, they decide to use the highest average conversion rates available from the report and ignore the other information. They send all the leads that were acquired through organic search to the sales pipeline. The remainder of the leads are put into a lead nurturing email journey.
Interfering factors:
Each lead is a unique person with different needs and behaviors. As the team uses a heuristic to choose the leads that should be transferred to the sales team, the decision they make is far from optimal. The conversion probability of a lead depends on many different demographic, behavioral, contextual and situational variables. These variables interact with each other, making it impossible for a human to take them all into account and find the optimal leads with the highest conversion probabilities.
Economic consequences: The overall conversion rates of the leads the sales team received have only improved slightly. Julia is disappointed. She expected a higher lift in the conversion rate after implementing the new approach. Many of the leads the sales team talks to are not ready for a sale yet. As these leads need more information before they can make a decision, the sales call did not happen at the right moment, therefore wasting budget and labor on unnecessary calls.
A day in the life (after)
New approach:
Julia orders an artificial intelligence from aaimo to calculate the conversion probability of each lead taking all the available data into account. The conversion probability is delivered right into the company’s CRM and is now available as an actionable insight per lead. The sales team can only handle fifty percent of the total amount of leads gathered. Therefore Julia’s team orders the leads by their conversion probability. The fifty percent of leads with the highest conversion probability are then sent to the sales team. The other fifty percent of leads are put into an automated lead nurturing journey which sends them more information via email about the different products and services the company is offering. Due to the lead nurturing activities the conversion probability of the nurtured leads changes during the automated lead nurturing journey. Aaimo’s artificial intelligence informs the marketing team which lead nurturing activities are improving the conversion rate most. The marketing team incrementally tests new lead nurturing activities and over time improves the lead nurturing journey further.
Enabling factors:
The artificial intelligence from aaimo individually scores each lead based on all available data. Only leads above a certain threshold of conversion probability are sent to the sales team. All other leads remain in the lead nurturing phase until they are passing the threshold because their conversion probability increased due to the lead nurturing activities.
Economic rewards:
The new approach to lead management increased the conversion rate of sales calls and the ROI of the sales team dramatically. The pressure on the sales team is reduced, as their capacity is optimally used. The sales team is feeling more motivated as their efforts lead to more conversions as they speak to the customers at the right time, when they are ready for a sale. The lead nurturing journey is developing leads towards being qualified for a sales call by nurturing them with more information until they are ready for a sale. Julia is very happy with their new approach to lead management, driven by insights from machine learning.
How can I increase the value of my leads?
With your data we can build an AI that will output the characteristics of a valuable customer. This information can then be used in lead generation campaigns to optimize the targeting towards prospects with these characteristics. The value you gain from your leads will increase which will raise your marketing ROI.
How can I attract more leads that actually convert?
We can use artificial intelligence on your lead data to predict which variables influence the probability that a potential customer will buy your product or service. If your company buys or leases email addresses or addresses for direct mailings, your marketing team can use the insights from aaimo’s machine learning model to optimize your targeting and get leads with a higher conversion rate. On your favorite ad channels such as Google Ads and Facebook Ads if you are in B2C or Linkedin if you are in B2B, the machine learning algorithm of the ad platform optimizes for attracting leads with a CPL as low as possible. This optimization for CPL can lead to a suboptimal lead quality as the ad platforms have no information about the leads actually converting to customers or not. If you optimize your targeting based on the output of our artificial intelligence, you will see higher quality and conversion rate in the leads you collect and thereby save a big portion of your media spend in each and every lead generation campaign you do.
How much time and resources should I spend on each individual lead?
We can use the data from your current customers to predict the future value that a lead would have if you could convert that lead into a customer. If you have this information you can better judge if you should invest more into the nurturing of this lead and which channel you should use to communicate with this lead. You can always nurture more leads via email without incremental costs, but you should carefully evaluate if each specific lead is worth a personal sales contact or another demo trial of your product.
Many companies struggle with a focused lead strategy although the necessary data is at their fingertips. We create and run artificial intelligence to process the data of our clients. Our clients have the opportunity to harvest the fruits of AI without investing in expensive IT infrastructure and hiring data scientists.
