Artificial Intelligence (AI) is having its day (year) in the sun. Previously Machine Learning (ML) was in the spotlight. But this time around with AI, we are seeing a lot of real-world adoption. AI is now being used in a wide range of industries, from finance and healthcare to retail and manufacturing. It is helping to automate mundane tasks and is making processes more efficient. AI is also helping to create new products and services that were previously not possible. This can be attributed to accessibility which makes the jump from aspiration to action very very short. Generative AI has a lot of effort put into the language interface making interaction and iteration very fast as the output is readily understandable. Who doesn't want a helpful sidekick to help you churn through data and work. Just hope that helper is MacGyver and not Cliff Clavin... both can sound smart and confident.
AI has made inroads into tools for marketers, sales professionals, and customer service teams. In this blog post, we explore some examples of AI in marketing, sales, and customer experience, showcasing how businesses are leveraging this advanced technology to make their teams more efficient and effective at driving growth and enhancing customer experiences. In the example below I'll be liberal with the definition of AI that includes ML applications.
1. AI in Marketing:
a. Personalized Recommendations: AI algorithms analyze vast amounts of prospect and customer data, including browsing behavior, purchase history, and preferences, to provide tailored recommendations (content, product, etc). As a former marketer, I might think... why wouldn't every prospect want to read/watch the amazing piece I spent hours pouring my heart into conveying why they need our products and services? But if we do some soul searching... every prospect is not only on a different time frame, they prefer different mediums and types of info to decide if they need a product/service. One size won't fit all well. We can personalize recommendations but looking at what is the buying role and level of the prospect and what they (or their peers) seem to like to consume... and recommend that content.
Similarly but in the reverse perspective. If you have created a new piece of content. You can look for prospects that would respond well to being sent this content by modelling the highly engaged prospect of similar content and scoring fit for this profile in your database.
b. Chatbots and Virtual Assistants: This has applications across marketing, sales, and customer experience. For Marketing AI-driven chatbots and virtual assistants can provide help during times of day when you don't have human coverage, and to help with low-value interactions to help triage incoming chats that need human intervention giving you more scale. These intelligent systems can handle simple well-understood queries, provide instant responses, and offer personalized recommendations acting like a concierge. For example, you might be promoting a user conference... you naturally think to update the banner but you can also update the Chatbot Scripts to help people sign up for the event or recommend break-out sessions etc.
c. Predictive Analytics: Similar to Recommendations but in the reverse perspective. AI-powered predictive analytics enables marketers to segment customers more effectively, predict their buying stage, and strategize targeted marketing campaigns to help them move along the buying journey. By analyzing historical data and external factors, such as demographics and market trends, AI algorithms can deliver valuable insights, empowering marketers to make data-driven decisions on matching segments to content. If you have created a new piece of content, you can look for prospects that engaged with similar content and scoring fit for this profile in your database that are likely to engage with this new content.
2. AI in Sales:
a. Lead Scoring and Prioritization: AI algorithms can analyze vast amounts of data to rank leads based on their likelihood to convert. This is far better than the rules based scoring that is still prevalent at many companies. AI/ML model based scores can take into account many more parameters than our brains can track and can find correlations between parameters. Because it's based in masses of data it's less likely to be biased by one's singular experiences. We feed the model prospects (people/companies) where we had the right outcome (closed won) and we ask the model to find similarities in their profile and activities... then we look at the incoming prospects to see how "good" their look-a-like score is and prioritize those that look like our ideal prospects (right company, right person, enough activity). This enables sales teams to focus their efforts on the most promising prospects, resulting in higher conversion rates and a more efficient sales process. There is still a place for Rules that override the models... and that's when we don't have data to support or model against.
b. Sales Forecasting: AI models can forecast sales trends by analyzing historical data, market conditions, and various other factors. If we make it easy and organic for the sales team to work their deals and keep them up to date with data. We can use AI/ML to model and predict size of deal, time to close (base on current pace), and could even recommend next best action (based on past similar deals). Accurate sales forecasts enable businesses to make informed decisions, optimize inventory management, and allocate resources more effectively. If there are shortfalls projected extra help can be provided for certain big deals or promotions can be made available. The key is knowing early enough to put programs in place.
c. Sales Process Automation: AI can automate repetitive tasks, such as data entry, lead nurturing, and follow-up emails. Imagine every salesperson had an assistant to help with these tasks. AI can help with this as long as we have well-understood and well-defined processes. Then it can recommend the next steps and the salesperson can confirm or can allow some tasks to be in autopilot mode. By offloading and streamlining these processes, sales professionals can focus on building relationships and closing deals, leading to improved productivity and higher revenue generation.
3. AI in Customer Experience:
a. Automated Customer Support: AI-powered chatbots and virtual assistants have revolutionized customer service. These intelligent systems can provide instant responses to frequently asked questions, handle complaints, and guide customers through the troubleshooting process, offering 24/7 support. The key to this is AI and retuning and never leaving the user at a dead end. If their need is not immediately fulfilled, connect them to a real person who can help even if it has to be asynchronous (i.e. email). Don't let it go into a black hole.
b. Sentiment Analysis: AI/ML algorithms can analyze customer feedback, including social media comments, reviews, and surveys, to understand customer sentiment. This helps businesses identify areas of improvement, address customer concerns proactively, and enhance overall customer satisfaction. A churn model is especially useful to understand if extra effort is needed to ensure the customer is happy and will renew/buy again. These can feed into automation programs that initially send recommendations on more and better usage of products/services and later if the issue is chronic assign a Success Manage to help them navigate how the company can help them with business challenges they are focused on.
c. Success Planning framework: AI/ML can be used to help draft a personalized success plan for the customer based on the info you have about the company's initiatives and how your product/service can help. Creating a series of milestones for success. This plan will be modelled against the milestones of successful customers who are further along in their journey. Having this generated is a great starting point that needs the Customer Success manager to review as they will often have additional context that is not easily found in the data. AI can also help automate some of the reminder and communication steps to focus the Customer Success manager on building relationships, coordinating internal resources, and quarterly customer reviews.
Conclusion:
AI has opened up a new realm of possibilities in marketing, sales, and customer experience. From automating routine tasks to providing personalized recommendations and predictive insights, businesses are using AI to drive growth, improve customer experiences, and stay ahead of the competition. AI is a trusted partner that helps with mundane tasks and summarizing research to help us get up to speed faster.
Especially because AI is still maturing, we have to remember it's a partner that we have to work together with just like our human partners. Treat it like a really smart intern who can do anything we point them at... but they can benefit from our experience and context to guide and prioritize their actions. By embracing AI-powered solutions, businesses can unlock time and unprecedented potential for success in all areas of their operations beyond the examples mentioned.
How are you planning/dreaming of using AI to improve your business? Share in the comments below and maybe others will help you flesh out the idea.
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