For entrepreneurs to succeed with their buyer advertising efforts, it’s important to grasp which clients are completely happy, that are susceptible to churn and which current cross-sell and upsell alternatives.
Your buyer knowledge is stuffed with clues that can assist you perceive which clients match into every of those buckets. You simply have to mine that knowledge to seek out the clues. Sounds easy, proper? It hasn’t at all times been that means. Then alongside got here generative AI.
For those who’re like most individuals, you hear the time period “genAI” and you concentrate on content material creation. However its capabilities are increasing and customers are experimenting with extra use instances.
For this text, I used the Google Gemini generative AI utility to assist me analyze buyer knowledge, determine clients that met sure standards and obtain suggestions and messaging to make use of with these clients.
Creating pattern buyer knowledge with Google Gemini
Earlier than I may analyze buyer knowledge, I wanted to create it. I constructed a state of affairs with Gemini to basically fabricate knowledge in a spreadsheet for patrons of a small B2B software program firm
I instructed Gemini the columns I needed within the spreadsheet and offered ranges for a few of the knowledge in an effort to stop some clients having lifetime income a number of a number of greater than others, for instance.
The columns I selected to create have been:
- Firm title.
- Variety of licenses for 2003 and 2004.
- Complete time logged within the app (2003 and 2004).
- Common time logged within the app (2003 and 2004).
- Lifetime worth.
- Buyer helps prices (2023 and 2024).
- Common month-to-month help prices (2023 and 2024).
One of many memorable moments from this a part of the train was once I determined so as to add year-over-year knowledge so I may run comparisons. Gemini added 2023 and 2024 lifetime values for every buyer. I identified that there’s just one lifetime worth, and Gemini instantly apologized for the error and stuck it.
All instructed, Gemini created pattern knowledge for 150 clients. Right here is the pattern knowledge for eight of them.
Let’s dig into buyer help prices
You possibly can study lots by analyzing help prices. Prospects with excessive help prices are in danger for churn. And clients that value a vendor extra in help than they convey in in income are unhealthy offers that have to be fastened.
That’s why my first question was to ask Gemini how most of the 150 clients had greater help prices than income. Fortuitously, none of them did, which is nice information. Subsequent, I needed to know which clients had the best help prices over the 2 years of knowledge we created.
Be aware that for annually the identical firms had the best help prices, which could possibly be an anomaly in our buyer knowledge, a misunderstood immediate or it could possibly be an indication of a bigger downside with the shoppers in query.
What do I say to clients whose help prices are killing us?
Since Gemini is a generative AI platform, it’s greater than able to taking the info and evaluation and drafting related communications. So I requested Gemini to draft an electronic mail to ship to clients with excessive help prices providing them sources to assist.
Gemini’s preliminary draft cited the excessive help prices within the electronic mail, which I requested it to appropriate as a result of it’s not sensible to inform your clients “You’re costing us a ton in buyer help!”
The second draft was nearer to what I used to be searching for:
Pricey [Customer Name],
We needed to succeed in out to precise our appreciation to your continued partnership with [Company Name]. We worth your corporation and are dedicated to offering you with the absolute best help.
That will help you get essentially the most out of our services and products, we’d prefer to give you entry to further coaching and sources. These could embody:
[Training Course 1]: [Brief description of the course] [Training Course 2]: [Brief description of the course] [Knowledge Base Article]: [Link to a relevant article]These sources can assist you and your staff troubleshoot issues extra effectively, prepare new customers successfully, and maximize the worth you get from our merchandise.
By benefiting from these sources, you possibly can doubtlessly cut back the necessity for help interactions and optimize your general help expertise.
Please tell us if you happen to’re fascinated with studying extra about these sources or you probably have another questions. We’re right here to help your success.
Let’s discover these churn dangers and get ’em fastened
I requested Gemini to determine within the knowledge the 5 clients most in danger for buyer churn. It identified that it was able to hypothesizing which clients have been most in danger, however that further knowledge, like historic churn charges for instance, would assist determine dangers extra precisely.
Right here’s a pattern from the 5 clients it recognized as churn dangers and why.
Figuring out churn dangers is barely half (and even much less) of the battle. So I requested Gemini to assist me define a retention technique for these clients.
A loaded query: Who’re the ‘greatest clients’?
Everybody in buyer advertising would like to know which of their clients are “the most effective clients.” However lots goes into defining a “greatest buyer.” And, as I anticipated, once I put the query of which clients from our dataset have been “the most effective” and why, Gemini jogged my memory we’re working with a comparatively easy dataset.
It may do a good higher job of answering the question, it mentioned, if it had data on:
- Buyer satisfaction rankings.
- Product utilization patterns.
- Churn historical past.
- Referrals made.
Nonetheless, Gemini took a shot at figuring out the most effective clients based mostly on the info we had round buyer lifetime worth (CLTV), help prices and engagement with the product.
What I discovered from this train
The pure language capabilities of Gemini and different genAI functions are getting higher. I didn’t have to create sophisticated prompts or ask Gemini to play a task. I merely requested it to do what I needed it to do.
Greater than spitting out solutions, nonetheless, Gemini added helpful solutions, reminiscent of further knowledge for our dataset that will be useful, or solutions round methods.
I discovered Gemini’s position on this train to be half simulator and half mentor. We have been utilizing fabricated knowledge, and whereas the train was fictitious, it was additionally very actual. This might have been precise buyer knowledge and the outcomes and solutions would possible maintain up. At the same time as a simulation, it made for a fantastic thought train.
The solutions and areas for enchancment Gemini supplied have been much like working with a extra skilled mentor. Gemini was proper in lots of instances. I didn’t add knowledge like buyer satisfaction scores, for instance, or referrals. Nor did I take a shot at including buyer acquisition prices. That’s the kind of suggestions a extra skilled marketer would possibly ship in a case like this.
My present plan is to maintain the info on the 150 fictional clients and add to it. I’ll proceed to ask Gemini to present me insights and solutions. I can’t wait to see what I study alongside the way in which.
Dig deeper: Meet my research team: Gemini, ChatGPT and Perplexity
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