5 e-commerce business challenges AI driven personalized marketing can solve
One of the biggest challenges for a marketer is implementing an effective personalized campaign—one that fits not just a target demographic, but their unique set of customers. But personalized marketing spans a vast spectrum of choices and dimensions, meaning there is no such thing as a one-size-fits-all personalized marketing campaign. The best way, the right way, the only way…those are all dependent on your business and the challenges you’re facing. That doesn’t offer much clarity, so we’ve created ABC-Commerce, a fictitious business that will help us illustrate the benefits of AI driven personalized marketing. We’ll be using them as our example throughout this post.
- Has a customer purchase history of several years to decade.
- Sells 3000 product categories online.
- Spends $1-2M annually on marketing.
- Has a purchase response rate of 2% .
- Utilizes direct mail catalogs and email marketing.
- Has 2 million existing customers.
- Has tens of millions of prospects/inquirers in their database
And, last but not least, ABC-Commerce is unable to identify customer segments most receptive to promotional offers and new marketing campaigns.
While virtually any business can use AI for efficacy, let’s take a look at five e-commerce challenges AI driven personalized marketing can help you solve.
Challenge 1: ABC-Commerce has a huge amount of customer data and each of their purchase details—but no idea what to do with it.
It all starts with leveraging the wealth of customer data businesses already have on hand. At this level, it doesn’t even need to be that rich. Basic transactional details, like who purchased what, when and where can provide a ton of great insights. And the more data you have, the better off you’ll be. What quantity did they purchase? What price did they purchase at? What source did they come from? Are they repeat buyers? Customer purchase history, especially a few years’ worth, contains strong signals around overall engagement and your relationship with customers in general. A layer beneath that lies deeper insights into each individual customer’s lifetime value and their likelihood to either convert or repeat purchases.
Your first step should be leveraging the customer data you already have. Discovering the details hidden in owned data is a fundamental exercise that even basic data wrangling and machine learning techniques can excel at. And while basic quantitative analytics are effective at explaining what has already happened, AI driven analysis can help you determine what will happen.
Challenge 2: ABC-Commerce is spending significant money on marketing with NO reliable conversion/response rates.
ROI for e-commerce marketing spend is a key metric to gauge the effectiveness of any campaign. Although absolute spend alone does not paint a clear picture of effectiveness, it is put into perspective when response and conversation rates fall below the industry benchmark set by companies using advanced machine learning predictive marketing. The best way for marketers to change the game? Gain a deeper understanding of the various micro segments of their customers— allowing them to uncover deeper insights into what composite of segments are performing well.
Of course, it’s challenging for many businesses with this type of profile to understand and identify historical trends. That’s a problem, because it’s impossible to know what will happen without full command of what has already happened. This is where AI comes in. AI plays a key role in analyzing historical data to cluster classify the segments likely to perform better for your business at any given point in time.
Challenge 3: Basic and rudimentary recency, frequency and monetary value are yielding little insight.
Tracking recency, frequency and monetary value of every customer is an age-old marketing practice. But is it really useful? Like other widespread practices we’ve already discussed, this data is primarily useful for providing historical insights. CLV (Customer Lifetime Value) is a critical marketing metric, but many businesses still calculate this based on historical spending, with nothing that factors in possible future purchases. Projecting expected spend over the next 3, 6, 9, and 12 months at each customer level can serve as a strong predictor for purchase likelihood. Using this data, customers can be segmented by likelihood and the confidence in those likelihood estimations to run effective AI driven campaigns.
Challenge 4: ABC-Commerce can’t correlate campaigns to sales.
It’s tough to judge campaign efficiency and effectiveness if you can’t tie it back to specific sales. But that attribution is just one aspect of the problem at hand. There are quite a few intricacies beyond simple catalog to conversion ratio. For example, how many catalogs do you really need to send to convert? How do you know whether the catalog played a key role in the customer’s purchase? AI learning algorithms can analyze historical data to identify deep patterns and correlations, allowing you to fine-tune and predict the precise patterns that result in a sale. That level of insight allows you to revise your campaigns and boost efficiency across the board.
Challenge 5: ABC-Commerce is increasing marketing spend with nothing to show for it
When marketing spend increases but response rate remains unchanged, it’s time to consider a revamp—preferably one utilizing advanced tools and techniques.
It’s easy to use the “eyeball test” when selecting a target market. Just pick the intersection of intersection of customer segments we find most intuitive. But it’s a huge pitfall to think humans can identify, let alone apply, the patterns hidden in historical data. Human intelligence just won’t cut it here. This is where AI makes a huge difference. AI can help us determine the right segments to target, freeing us up to focus on designing creative campaigns sure to resonate with the customers AI has identified. It’s easier AND more effective. What’s not to love?
AI driven personalized marketing holds the power to revolutionize the way you target and market to customers. It’s flexible, adaptable enough to suit any business. It’s effective, combing through massive amounts of data to take out the guesswork on your end. And it’s affordable, capable of meeting your marketing goals without costing a tremendous amount on your end. It’s probably time you give AI a try. After all, the smartest way to market is with the smartest tools.
Thanks for reading the post. Please share your thoughts/ideas/comments on how you are using AI for your marketing.
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About the Author: Veda Konduru is the Founder and CEO of VectorScient. In her current role, Veda is responsible for crafting and executing VectorScient’s strategy. Veda also serves as Chief Product Architect for VectorScient’s Predictive Marketing Cloud. Prior to launching VectorScient, Veda spent more than 10 years in corporate world, architecting and building software applications for Sales, Demand Forecasting and Supply Chain, with reputation for excellence in product quality, Customer Focus and engagement. Veda blends her Statistics background with Machine Learning and Data Science to create truly world-class Predictive Analytics product. She holds Master’s degree in Computer Sciences.
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