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Using AI to cross-sell and upsell

Using AI to cross-sell and upsell

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Increasing revenue, particularly with a good margin, is a major focus for any business. Many managed service providers (MSPs) find themselves in a situation where they have gotten off the ground and survived mainly through gaining passing trade with strong use of search engine optimization (SEO) that has attracted a base of users. Many of these customers may have told others about the service they have received and recommended the MSP.

However, as the MSP market has expanded, passing trade is almost non-existent. MSPs had to become more like the ‘old’ companies they thought they had replaced. They now tend to have sales and marketing departments tasked with increasing revenues and margins.

Strength of existing customer relationships

Gaining a brand-new customer is generally more expensive than upselling to an existing one. Existing customers are already aware of who you are and what your base offerings are. The health of the existing relationship is key. If the relationship is bad, the chance of selling additional services to the customer may be low. Having a good idea of the state of the relationship provides a starting point.

This is where artificial intelligence (AI) can come into play. Rather than putting all the emphasis on humans trying to work from the ground up to create a new revenue stream, AI can do a lot of the groundwork to make life easier.

Ways to use AI in this case:

  • Analyze what a customer is actually doing. We can use AI to determine the activities the customer is engaging in. This could include how many users are using a particular service; how effective outcomes are; and, whether usage levels are going up or down. Based on this analysis, it may be a good first step to point out to a customer how much they could save by changing how they use your services. Certainly, this doesn’t establish a new revenue stream, but it contributes to building a better relationship where the customer may trust the MSP more when a new sale is attempted. In fact, generative AI can directly generate reports that the MSP can send to the customer, containing advice, recommendations, and opportunities for them to access additional services that can benefit their organization. The more access a customer can provide to their data and workflows, the more an MSP can help them in the future. However, the MSP must maintain full data security and not divulge any information on the workflows the customer uses.
  • Carry out sentiment analysis of the customer base. There should be a large amount of data available for an MSP to mine to see how a customer feels about the relationship between them and the MSP. The help desk should be the first point of contact. How many problems did the customer have, and how quickly and successfully did they address them? Did they result in any positive or negative feedback? We can also include other information from account managers and salespeople to provide a more complete picture. If things are perceived to have gone wrong, AI may provide insights on how to retrieve the relationship.
  • Assign a level of probability that a cross-sell or upsell will work. Based on its analysis, businesses can use AI to determine how likely a customer will accept any cross- or upsell offers. This can result in less time wasted on low-likelihood sales and more time devoted to high-likelihood ones.
  • Reduce costs and human errors. Automated use of simple and generative AI reduces the need for human intervention, which tends to be costly. It can also avoid errors, such as using wrong data and information when contacting and dealing with customers.
  • Create meaningful communications. The larger an MSP is, the harder it is to maintain communications with many customers at any meaningful level. Instead, companies often send out generalized communications – messages that only interest a portion of the customer base, which the rest may perceive as too impersonal. Even for smaller MSPs, generative AI can provide a better level of meaningful contact regularly, helping the customer feel more like part of a relationship – rather than just a paying customer.
  • Optimize revenues and profits. Any actions that do not provide a payback for the MSP are useless. AI can identify which leads the salesforce should quickly follow up on a personal basis. It can also identify which needs further AI input to make them more likely to lead to a sale.

Navigating the AI landscape

There is, as always, a caveat around all of this – and it is a major one at the moment. Putting 100 percent faith in AI is dangerous. The technology is still too immature to be trusted completely. As such, one should view it as an initial investment in cost. AI will not replace much but businesses must see it as something that works along with existing salesforces and processes while putting in place requisite checks and balances to ensure that nothing horrendous goes out that is likely to lose a customer. Only as the workforce gains trust in AI output should they fully automate workflows using it. Even then, it’s essential to maintain a check on a certain percentage of output for a longer period.

However, AI is here to stay, and those who use it effectively will be the winners in the market. It is best to be well prepared by looking at how it can fit in with an MSP market approach now and be ready when required rather than trying to react in a knee-jerk manner when the rest of the market is already using it.

Photo: Dilok Klaisataporn / Shutterstock

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