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Generative AI & Loyalty

Generative AI & Loyalty

One thing became very clear while attending Shoptalk in Las Vegas several weeks ago. The hottest topic was generative AI. 44% of attendees surveyed wanted to see generative AI case studies and to learn more about its potential in helping drive business performance. An interesting follow up question might have been, “What benefits do you expect from generative AI?” Followed by, “Do you know the difference between generative AI and machine learning?”

Do you know the difference? Under the banner Dummies Guide to Generative AI vs. Machine Learning, we share the following:

Generative Artificial Intelligence (AI): describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. The most commonly used generative AI tool is ChatGPT which, for us dummies, stands for generative pretrained transformer. Another common generative AI tool is image generator DALL-E (an ode to surrealist artist Salvador Dali and Pixar robot WALL-E). Both tools have great potential to impact your job performance and that of your marketing partners as well so if you are intrigued so far…read on!

You might think that voice assistants like Siri and Alexa were founded on Generative AI technology, but these AI tools rely on Natural Language Processing (NLP), which is a Machine Learning technique!  The most universally understood application is your viewing habits and engagement on Netflix.  It helps inform what shows and movies are shown in your personal view while on the main page of the service experience.  Whether you love Romantic Comedies or Documentaries, machine learning-based algorithms learn your preferences and try to predict content you will love. Machine learning also drives the web pop-ups and retargeting banners that follow us around the internet.

In the most important area to marketers, driving business value, Machine Learning is where the proverbial rubber hits the road. All the buzz is about Generative AI, but indeed, the more “practical” and actionable tools and solutions that marketers use today primarily leverage machine learning. If you’ve evolved from basic modeling and segmentation to machine learning over the past 10 years, you might be surprised to know that this was enabled through supervised machine learning models that both predict and classify. Indeed, you may have also used some unsupervised machine learning models to segment and cluster your data. Supervised learning models use labeled training data and employ a hypothesis of what correct output should look like. Unsupervised learning models do not.

The best method for determining where to use a Machine Learning vs Generative AI algorithm is to identify whether the task involves working with language, images or numerical data. For language-based tasks, generative AI is likely to provide the best results. Conversely, for numerical analysis, Machine Learning techniques are required. In fact, generative AI is not good at working with numerical data. Producing new images from text prompts (language) is a generative AI task. At present, analyzing existing images (computer vision) is actually a numerical task and best performed by machine learning models

So, are you ready to begin?

  • Step 1: Identify Quick Wins – To start, begin with a small list of low-effort, high-impact business opportunities. Middle-management leaders across the organization probably have a list of things they would love to get fixed. Talk to them and find the necessary but rote tasks that take too much time and effort and keep the business teams from getting to the core value driving activities. AI is not a tool to replace jobs, but for automating tasks and amplifying our human capabilities. So, focus on finding a candidate list of about five potential task improvement/replacement opportunities where the business teams would embrace even small improvements in their quality of life. With this opportunity list in hand, you are now ready to start learning about your data and the skills and solutions needed to capture these opportunities.
  • Step 2: Experiment with an Expert Partner – For those without internal AI expertise, it is important to find a partner who understands the problems you have identified, has solved the same or similar issues in the past, and is willing to run lean and fast. Don’t be afraid to shop around. There are many boutique AI consulting firms, software companies, dedicated analytics firms, and others who will be desperate to help you. The key is to find a partner with whom you are comfortable, where you have confidence in their knowledge, and where they are willing to align to your interests. Specifically, you want a partner that will work in 2-to-4-week sprints, where if you are not happy with progress, you can pull the plug on the contract.
  • Step 3: Build Organizational Knowledge – Developing knowledge about artificial intelligence should be an organizational imperative. Increased organizational AI knowledge will lead to broader adoption and to the competitive benefits that AI delivers. Hiring knowledgeable talent should clearly be an important consideration, but educating the broader organization is more important. To really benefit from AI, your organization will need people at all levels to understand how it works and how it can be applied. This does not mean that they need a technical level of proficiency, but they need to at least have some foundational knowledge that allows them to adapt and apply this new technology.
  • Step 4: Develop and Communicate an AI Strategy – The best AI strategy will align with your objectives as a retailer. Just as other significant organizational investments are evaluated against the objectives of the business, so should AI investments. Ideally, you will deploy AI in ways that improve your competitive differentiation and create an advantage for your business. For example, suppose you are trying to provide an everyday low-price product to your customers. In that case, using AI to drive cost and inefficiency out of the supply chain should likely be a primary focus area. Alternatively, if your business is focused on differentiated products, where every penny of cost is not as critical, then using AI to enhance product design, create better customer experiences, or engaging social content could all be essential parts of your AI strategy.
  • Step 5: Address Barriers to Progress – As you attempt to implement AI in your retail organization, you will encounter barriers to progress in many areas. This is a normal part of the change process. AI comes with more negative media baggage than most other forms of organizational change and should be tackled as an expected part of the AI transformation process. In addition to organizational adoption issues, you will run into issues with data, technology, talent, and much more. Some of the barriers will be specific to AI, but many will be typical to most of the project-based work your organization will have done many times before. Having a strong project manager, or PMO, that can keep you organized in the resolution of these various issues will be highly beneficial.
  • Step 6: Repeat! – Few retail organizations have an appetite for multi-year mega implementations these days. The marketplace is too dynamic, customers are too fickle, and employee turnover is a constant challenge. Not to mention, there is intense pressure to deliver immediate results. AI has now developed to the point that large arduous implementations shouldn’t be necessary. In fact, you should be wary of any suggestion to the contrary. There are certainly some exceptions, but the beauty of modern AI is that it should be able to move at the speed of retail. So, once you have reached this step, it is time to revisit your business objectives and pick the next set of valuable improvements where AI could make a difference to your business.

Profitmind provides an awesome Playbook that you can download to inform your path forward and includes more details on each of the above steps. By following the steps shared, you can establish a solid foundation for a successful project while mitigating risks and maximizing the chances of achieving your desired outcomes.

In regard to loyalty program strategies and tactics, think specifically about any one or more of the following as your first step as loyalty program owner to then become both a generative AI and machine learning ninja!

  • Customer Segmentation: AI can analyze customer data and segment customers based on various attributes such as purchase history, demographics, and behavior patterns. This segmentation can help in identifying different groups of customers with varying preferences and loyalty levels.
  • Predictive Modeling: Algorithms can forecast future customer behavior, such as likelihood to churn or engage with specific promotions. These predictions can inform loyalty program strategies by enabling personalized offers and incentives to retain customers and drive engagement.
  • Content Generation: AI can create personalized content for loyalty program members, including targeted emails, messages, or rewards recommendations. This tailored content can enhance customer experience and strengthen loyalty.
  • Dynamic Pricing: AI can optimize pricing strategies within loyalty programs by analyzing demand patterns, competitor pricing, and customer willingness to pay. This allows for dynamic pricing adjustments and targeted discounts to maximize customer retention and value.
  • Recommendation Engines: AI-powered recommendation engines can suggest relevant products, services, or rewards to loyalty program members based on their past behavior and preferences. These recommendations drive upsell and cross-sell opportunities while increasing customer satisfaction and loyalty.
  • Fraud Detection: Algorithms can detect fraudulent activities within loyalty programs by analyzing transactional data and identifying anomalous behavior patterns. This helps in safeguarding the integrity of the program and maintaining trust among members.
  • Feedback Analysis: AI can analyze customer feedback, including reviews and social media interactions, to identify areas for improvement within the loyalty program. This continuous feedback loop enables iterative enhancements to the program’s structure and offerings.
  • Personalization: AI enables hyper-personalized experiences for loyalty program members by dynamically adapting rewards, promotions, and communications based on individual preferences, behavior, and lifecycle stage.

In the future, generative AI will play a crucial role in loyalty and affinity programs through personalized interactions and member experiences via chatbots and website content. Generative AI can deliver the 1:1 personalized experience that members “expect” from today’s digital channels by customizing what they see, how they interact and what offers are presented in any given session. The next site visit or call you make to a customer service center or chat could be AI-enabled incorporating predictive logic to improve the experience, drive conversion, prompt for first-party data or any number of new insight requests.

A simple example is building a generative AI capability that takes customer feedback into a database to produce reports/dashboards and strategic recommendations for business management on how to improve customer satisfaction. Your challenge in 2024 is to explore generative AI with your analytical partners and seek opportunities to improve customer interactions at every touch point, at scale. Better customer service (faster, more consistent, more courteous, more accessible – no wait times), and to produce truly personalized content that will get your valued customers to say “they’ve shown me that they know me”.

To learn more, reach out to us at Ascendant Loyalty for a free strategic planning consultation to help you get started. Plus, we will introduce you to our strategic partner, Profitmind (from Netail, Inc.) whom we work with every day to deploy the latest AI capabilities in both retail and loyalty.

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