Data Science, Machine Learning & AI
We bring the power of data science and artificial intelligence to every business
Improving customer experience and creating new and engaging ways of interaction with customers – this is today’s number one priority for your company to grow and succeed.
We harness the power of AI and machine learning technologies with a view to implement the cutting-edge approach to ensuring exceptional customer experience.
Once high-quality data is available in a consumable fashion, the next step is to identify the AI-ML use cases for the organizations. Here are the most common use cases that marketers can consider in order to personalize the experience for the customer.
The essence of strategy is the concentration of effort and energy. Market Segmentation helps a company or a brand focus its marketing energies to maximize the return on strategic marketing investments.
- Market Segmentation answers four basic strategic questions:
- What market segment or segments should be targeted?
- What is a brand’s optimal positioning to reach its targets?
- What advertising themes and messages should be explored?
- What new product opportunities exist within a given marketplace?
Customer churn analysis refers to the customer attrition rate in a company. … Gathers available customer behavior, transactions, demographics data and usage pattern. Converts structured and unstructured data/information into meaningful insights. Utilizes these insights to predict customers who are likely to churn.
Customer LifeTime Value
“The purpose of a business is to create and keep a customer” – Peter Drucker, Management Consultant.
The lifetime value of a customer, or customer lifetime value (CLV), represents the total amount of money a customer is expected to spend in your business, or on your products, during their lifetime. This is an important figure to know because it helps you make decisions about how much money to invest in acquiring new customers and retaining existing ones.
The Value of Knowing Your CLVCalculating the CLV for different customers helps in a number of ways, mainly regarding business decision-making. Knowing your CLV you can determine, among other things:
- How much you can spend to acquire a similar customer and still have a profitable relationship
- What kinds of products customers with the highest CLV want
- Which products have the highest profitability
- Who your most profitable types of clients are.
Not that long ago, people lived and functioned in tight communities. Every vendor knew their customers personally and could make recommendations to them based on a personal knowledge of past purchases. This type of personal relationship meant that customers would receive great customer service, while vendors were able to reap the benefit of brand loyalty since they understood their customer’s needs, preferences, and even their budget.
Fast forward to today, and our modern-day globalization of services and we see that things have changed. While we gain a lot regarding productivity and availability of options, we lose that intimacy of personal client-vendor relationships.
However, this lack of personalized service doesn’t change the very important fact that the key to successful sales is understanding a person’s problems.
and there is an algorithm for that!
Marketers today have their work cut out for them. The average customer uses 10 channels to communicate with companies, which means the digital marketing landscape is more fragmented than ever. As customers’ expectations rise, so does the temperature in the proverbial kitchen for marketing management.
Marketing attribution is the way in which marketers assess the value or ROI of the channels that connect them to potential customers. In other words, it’s the means by which the customer came to know and buy your product or service.
With so many touchpoints to consider, operational marketing roles are becoming more and more complex. Luckily, there are a number of marketing attribution models that have been introduced and evolved since the digital boom to account for multi-channel selling.