Data science is the secret sauce of Customer Experience Transformation
There has been a lot of talk about the power of data in business strategies, however data alone is not valuable. The value is attributed when you know how to use them to your advantage, leveraging interpretations to improve company performance, whether it’s for revenue growth, gaining a competitive edge, or increasing operational excellence. This is where Data Science comes into play.
It is nothing more than the science used to collect data and organize it in an applicable way so that it brings direct benefits to the business. Therefore, it is essential to transform them into valuable information, addressing one of the most significant concerns that often hinder organizations from moving forward when it comes to providing an excellent customer experience, which is disconnected data.
In this new era of consumption, paying attention to customer relationships and finding ways to provide a better shopping experience is increasingly mandatory in the market, regardless of the industry. To understand how Data Science can be a powerful ally for this purpose, follow this article prepared by Lima Consulting‘s blog.
Data Organization and Ownership
As seen, when data is well-managed, it enables advertising companies to be more assertive by providing a broader and deeper understanding of consumers. At the same time, it generates insights for the development of new marketing and sales strategies for products and services.
First-party data and the importance of proprietary data
In this regard, first-party data holds immense importance. Owned by the brand/company, they can – and should – be leveraged as part of your digital strategy. This technical term, also known as primary data, refers to the information generated about your customers from your own sales, research, and promotional channels.
Why is this important? Firstly, these data are YOURS. Therefore, you are responsible for them and can use them to benefit your business. If a potential customer has provided contact information or personal data to you, it means they have an interest in your products or services.
Furthermore, in the realm of first-party data, the customer has already entered the sales funnel, making the conversion process much smoother. With this type of data, the marketing team can work to produce more personalized content for your target audience. This is where brands can increase their revenue by understanding what their users want to consume.
As mentioned earlier, primary data is collected from the company’s domains, which are proprietary channels adopted by the company for data capture. This can include:
- Website: Collecting browsing behavior and event interactions on the site, such as internal searches, product views, navigation sources, interaction with campaigns, or interest in specific topics.
- App: Complementing information with user behaviors within the app, device usage, video views.
- Email: Gathering email open rates and interactions with the “call to action” in emails, campaign types.
- CRM: Customer registration data, preferences, and loyalty, including VIP customer segments or additional classifications.
- POS: Transactional data from points of sale that can be associated with consumers to unify offline and online data.
The more you know, unify, and personalize communication with your users, the greater the chance of improving engagement and financial returns. However, don’t assume they will give away their data without something in return. Trust is required for this.
The best way to acquire first-party data is by offering rich and interactive content that generates engagement, such as infographics, landing pages, questionnaires, and other formats. In addition to the information obtained with primary data, companies should also invest in proprietary algorithms to have ownership of their own predictive models, whose function and importance will be discussed below.
Predictive Models and Integrations
It’s essential to look at past data, but with the aim of being predictive, assuming customer intentions based on their historical behavior. What adds value to the customer experience is the ability to anticipate consumer behavior. It is part of the company’s commitment to provide resources that anticipate the needs of its audience, offering solutions to their problems and, in this way, improving the consumer’s experience as a user.
Since there is no magic formula for predicting the future, the solution lies in the use of predictive models, whose objective is to detach decision-making from intuition and instead base it on statistical predictions from data analysis. Basically, these models consist of one or more mathematical functions applied to a large volume of data that can identify patterns and future trends.
Even when operating in different sectors, companies often face the same challenges, which can be overcome with the help of data. Who has the potential to become a recurring customer? Which products will be in higher demand next month? How to reduce the churn rate? These are some common and generic concerns in the business world that predictive marketing can help solve.
The construction of predictive models is typically a specialty of data scientists. It has become increasingly feasible with the constant evolution of machine learning (ML) packages and tools worldwide. They have greatly assisted in scalability, automation, speed, and accuracy of results, another contribution from the field of Data Science.
Returning to the previous questions, predictive analytics platforms can now integrate with various data sources where answers are hidden – CRM, ERP, POS, and much more. This way, information can be extracted to create predictive models relevant to your specific business concerns, as the goal of data analysis can vary according to the company’s intentions.
LTV
With LTV (Lifetime Value) models, it is possible to determine, for each customer, the expected financial return over a specific time period. By aggregating this information in advance, there is also the possibility of forecasting the future ROAS (Return on Advertising Investment) of a campaign to guide optimization decisions, allowing your team to make confident choices.
Anticipating the campaign’s ROAS provides insight into its long-term potential. This will provide invaluable knowledge about the future and insights into how your campaigns will meet marketing goals and organizational KPIs.
Furthermore, achieving a higher ROAS is crucial for helping your company acquire new customers and meet and exceed growth targets. Identifying which campaigns will aid in this at the early stages of their lifecycle will lead to better budget utilization and improved outcomes.
Churn Prediction
Churn is a common problem and one of the major concerns faced by many businesses because no one wants to lose customers. When the rate of cancellations reaches certain levels, the impact becomes significant on profits and the long-term sustainability of the business.
You may have heard that acquiring new customers is more challenging and expensive than retaining existing ones. Therefore, losing customers is something that companies should strive to avoid, and one efficient strategy for this is churn prediction.
Churn prediction is a process that uses Machine Learning models to determine the probability of a customer discontinuing the use of a product or service, essentially leaving your customer base. With this prediction, it is possible to make proactive decisions to minimize the chances of cancellation.
Some data that can be collected to anticipate churn include:
- Purchases made
- Frequency of product or service usage
- Customer feedback
- Support complaints
- Actions taken by customers with similar profiles, etc.
This data will be used to train ML models to accurately predict the likelihood of a customer leaving the company based on identified patterns.
With this information, coupled with marketing tools that allow you to “activate” this data through audience segmentation and automated communication across multiple customer contact channels, it is possible to reduce churn through more effective campaigns targeted at customers at potential risk.
Predictive Analysis in Next Best Experience
Next Best Experience (NBX) is an analytical paradigm that allows identifying and delivering the right experience to the right customer in real-time based on everything known about them. To achieve this, signals gathered throughout the customer journey are required.
This experience can range from customer service or engagement to operational, product, financial, sales, or marketing experiences.
How to Determine the Next Best Experience?
The NBX strategy should be grounded in evidence – longitudinal, behavioral, intent, and satisfaction data from all journeys are most valuable at the outset. Essentially, any information about customers over time will have more value than data reflecting a single moment.
With Data Science techniques, it’s possible to personalize the next best experiences based on what will generate the most real value for the business. Most companies focused on maximizing profits aim for Customer Lifetime Value (CLV), which is the metric that measures the predicted revenue generated throughout the customer’s lifecycle. It’s worth noting that CLV is highly complex because it requires personalization at scale. While LTV is a consolidated metric that aggregates data from the base, CLV uses statistical models to obtain individualized projections per consumer. Therefore, a high-level technical team is required to build this information.
To optimize NBX, customer journey automation technology and real-time interaction management (RTIM) will be allies in synchronizing channels, sending and receiving customer data, making decisions, and then providing communications.
It is necessary to focus on ensuring data flow between these channels and eliminating drop-off points in journeys. By connecting all your data and channels, you can ensure that consumers do not unnecessarily repeat experiences – nor see irrelevant information.
Data Science for Product Recommendations
Recommendation systems are undoubtedly one of the greatest contributions of Data Science to customer experience and a company’s bottom line. It’s a tool used by major market players to personalize the user experience, such as Netflix, which uses it to recommend content to its subscribers.
Through purchase data, it’s possible to personalize the website with a selection of products most relevant to the user’s profile. These systems also filter user choices based on their previous searches or the searches of other customers with similar behavior. The three most commonly used recommendation techniques are Collaborative Filtering, Content-Based Filtering, and Hybrid Recommendation Filtering.
Furthermore, through data analysis, companies can gain a greater understanding of how their upsell (selling higher-value items) and cross-sell (selling additional or complementary items) strategies will perform well.
With this, Data Science is used to provide personalized cross-selling recommendations, suggesting additional products that a customer may be interested in purchasing. It also helps identify key sales parameters, such as key value items, key value categories, popular products, and high-demand products.
This has a positive impact on customer experience, which, according to Gartner, generates over two-thirds of customer loyalty, more than brand and price combined. Consequently, companies that offer a personalized experience demonstrate a competitive advantage by standing out in the market and becoming more attractive to consumers.
Data Science for Customer Clustering
Creating similar audiences with the help of data analysis makes segmentation more precise. It’s no coincidence that Data Science is also used to strategically expand a company’s consumer base by focusing on those with profiles resembling those of future high-value customers.
This strategy of segmenting customers based on their buying habits and informative data to plan, optimize channels, deal with negative social media feedback, allocate budget, and tailor content to their needs will result in more effective campaigns.
This brings us back to optimizing the customer experience, as the competitive potential of your offerings increases when they are personalized. You can offer services or products tailored to the true demands of your ideal customer, based on Data Science, which helps understand what potential audiences really want, adjust offerings, and outperform the competition.
In addition to smooth processes, directing marketing efforts and prioritizing potentially more profitable customers is essential for the financial health of organizations as it enhances Return on Investment (ROI).
Count on Lima Consulting
Are you seeking digital transformation for your company to enhance the customer experience? Count on the specialized consultancy of Lima Consulting Group. In this article, you could understand the contribution of Data Science to customer satisfaction and business operations. If you desire innovation for your business’s digital future, get in touch with our team of experts and learn how we can assist you.
