Business data is more than an operational byproduct, it is a viable strategic asset for growth, efficiency, and customer experience. Raw data does not lead to an increase; it is the insights derived from data, and the actions taken with those insights that yield transformation. Responsible data-driven companies utilize data to the fullest in optimizing their pricing, optimizing promotions in their marketing, enhancing their service quality, and building deeper, more profitable customer relationships.
How can your organization unlock business data for growth and customer success? Here are the 12 best use cases. For each use case, we also share examples and measurable business impact.
Use Cases of Business Data for Driving Growth & Customer Success
Understanding Customer Behavior & Segmentation
Understanding your customers’ behavior is key to any growth strategy. When you roll up the customer, browse, buy, and engage with your brand data, you’ll see behavior patterns that lead to practical actions. Behavioral segmentation is when you divide your audience into meaningful groupings based on the similarities of behavior, like purchase frequency, product choice, or time to purchase or engage.
For instance, a SaaS company that charges customers a subscription fee may identify behavior segments: power users – who log in daily, casual users – users who log in weekly, and at-risk users who have not logged in for multiple weeks. Each of these segments of customers could receive an appropriate contact – rewards for loyal customers, a feature intro for casual users, and a win-back tactic for the at-risk users.
You can build the behavioral segmentation from various data sources, such as website or app usage, email communication, CRM platforms, and purchase histories. Using segmentation fully allows for greater personalization, better engagement, and more efficient marketing spending.
Business Impact: You are getting very targeted communication when you do segmentation, which translates into higher conversion rates, better customer retention, and more relevant touchpoints with your customers. More importantly, these customers feel understood, which leads them to be loyal and increase their lifetime value. Behavior segments provide relevant information to take action on, rather than abstract information from numbers to see, understand, and act on to drive revenue and satisfaction.
Data-Centric Pricing Models
Pricing is a lever for profitability, and data-centric pricing is a way to reduce much of the guesswork. By examining how the company performed historically compared to competitors, demand elasticity vs pricing, it is possible to discover price points that optimize sales and margins together, rather than separately. Factors in the company’s performance can lead to dynamically priced solutions vs fixed rate systems. The leading firms now drive pricing dynamically using real-time information to manage and act upon current market conditions.
As an example, an online retailer could leverage AI resources to manage real-time price changes based on seasonality, competitors’ pricing interventions, inventory availability, buying behaviors, etc. If a major competitor instantly drops the price low enough to meet their demand, the system would react instantly to still maintain price profitability, even in the face of hardship.
Similarly, an intense demand phase can yield opportunities to increase pricing because it is possible, and still enables the firm to realize that value, instead of being reluctant to make changes to achieve that value.
Examples of business enabling tools include price optimization software, competitor tracking software, and predictive analytics models.
Business Value: Having a data-driven pricing architecture improves competitiveness, reduces margin erosion, and also increases customers’ perceptions of value! It becomes a win for both parties since businesses collect higher revenue and customers are confident that they will pay a fair price based on the current market conditions.
Predicting Churn & Increasing Retention
Churn prediction is one of the most valuable use cases for business data. Using your customer data to find customers in danger of churning before they churn allows organizations to be proactive and ultimately work around their dissatisfaction with customized remedies.
Churn prediction models can leverage patterns in customers’ actions, such as lack of engagement, missed payments, and poor customer service experiences, to highlight customers or customers with other damaging behaviors who may be on the verge of churning.
For example, as a SaaS, you may be tracking user log-in frequency, products used, and support ticket sentiment for an account. When you see the customer suddenly attending logins, using fewer products, and providing negative feedback, the system would flag that user and notify the customer success team that they are experiencing churn risk. The customer success team could then provide ideally tailored and targeted support, offer onboarding support, or offer retention incentives to redeem some accountability and earn the trust of the account again.
Relevant and useful data inputs for churn prediction include customer product usage analytics data, customer relationship management (CRM) data, and transaction history data.
Business Impact: Preventing churn is way less costly than acquiring customers. Preventing churn protects recurring revenue, increases customer lifetime value (CLV), and increases customer loyalty. When customers feel a business is trying to identify and provide solutions to preempt churn, they feel valued, which leads to sustainable long-term growth and success.
Improving Product Development Using Feedback Loops
To be successful in our efforts, we need to satisfy customers. The best approach to guide decision-making on whether and what to build next is data. It can start with surveys and feedback collection, and include analyzing feature usage and in-app behavior to form a legitimate feedback loop of continuous improvement.
For example, let’s say an application developer has discovered in their app analytics that usage of one feature is high and another, similar feature is low. If the low feature is also related to even higher drop-off rates in in-app usage, then the app development team can remove that feature or improve it. Feedback gathered from beta testing and NPS surveys can help ensure that development and planning priorities align with user expectations.
Sentiment analysis through social media can help surface an emerging demand or customer frustrations. The feedback and adjustment loop further helps refine existing products and contributes to the roadmap of future product releases.
Business Impact: If you can build products for verified demand by the user, you will be burning fewer assets, grow user adoption faster, and increase user satisfaction. Customers see that their feedback influences your product development decisions and, therefore, builds their trust and loyalty to your product. And what grows over time is a product ecosystem that customers like and advocate for.
Hyper-Personalized Marketing Campaigns
Personalization used to be a luxury in marketing. Today, in a noisy digital marketing environment, personalization has become a necessity for competitiveness. Using demographic data, behavioral data, and transactional data, retailers can create personalized and customized campaigns that align with the individual interests of each customer.
For example, a retail brand may consider a customer’s purchase history and browsing data to recommend complementary products. A customer who buys running shoes might get offered related products such as sports socks, hydration packs, or training plans.
Personalized marketing can also include timing. In other words, sending messages when customers are more likely to engage with them. AI-driven platforms can identify these moments of optimal engagement for your customer, based on their previous engagement history.
Business Impact: Personalized campaigns consistently outperform non-personalized campaigns and are typically 2-3x better than non-personalized campaigns. Customers want to feel seen and heard, and personalized campaigns begin to develop that recognition. Their emotional connection to the brand is enhanced, and marketing ROI is maximized, creating stronger and deeper relationships in the long term.
Sales Forecasting for Strategic Planning
Sales forecasts are essential for formulating realistic targets, positioning stock, and allocating resources. Organizations can review sales history, overall pipeline health through their CRM, and a variety of external market factors to provide greater accuracy when predicting revenue.
A B2B organization that sells an unpredictable product, such as machinery, could manage a combination of pipeline stage probabilities with close rates from the past and perhaps adjust for seasonal trends and product cycles. It allows the organization to size demand with trusted predictions, budget accurately, and organize “recall stock” where necessary, methodically size sales and staff, as well as product.
The type of forecasting software used will determine whether the organization reconciles the probabilities versus past close rates internally or incorporates AI to account for unforeseen changes in the market externally. In the end, an organization can be more nimble and agile by leveraging the right combination of AI forecasting systems.
Impact on the Organization: Reliable forecasts improve operational efficiency and decrease the costs associated with overstocks or stockouts and contribute to a better financial planning process. Having confidence in forecasting allows leadership to make confident investments when opportunities exist, and decrease risk in purchase planning while following through on growth opportunities.
Optimizing Customer Support Using AI and Analytics
Customer support interactions yield valuable data and insights about common pain points. With the help of analytics and AI tools, businesses can identify frequent issues, error resolution times, and customer satisfaction with support interactions.
Say a telecommunications provider analyzes thousands of chat transcripts using Natural Language Processing (NLP) as part of its customer support team. A provider can identify if 30% of interactions are due to customers not understanding their bill. The provider will have the option to change their FAQ, improve bill clarity, or implement self-service use cases so that the provider can eliminate incoming tickets and let the remaining agents deal with more complex requests.
Automation of ticket routing and self-service request options can also increase the use of chatbots and create more efficiencies for routine requests. Additionally, utilizing an AI tool for sentiment analysis will help ensure that priority cases are addressed more quickly.
Business value: Implementing a data-driven approach to support can enable organizations to improve response time, reduce operating costs, and better impact customer satisfaction scores (CSAT). In addition to customers receiving faster, more accurate support, organizations also can build relationships with their customers, ultimately creating happy, loyal customers.
Geographic expansion decisions
There is risk in entering new markets if organizations don’t truly understand local conditions. Organizations use data to demonstrate regions of interest for possible expansion, while they also analyze demographic shifts, levels of income and purchasing power, competitors, trends, cultural aspects, and other, local conditions.
In assessing the potential expansion of a fitness chain and a new regional location, for example, you could be analyzing the regional income levels, age distribution, and lifestyle preferences. If your data shows pockets of disposable income relating to young professionals and much less competition, that city probably has a better chance of successful expansion.
With market research platforms, census data, and competitor insight, you will be able to substantiate decisions based on your insights and recommendations.
Business Impacts: Data-led market entry allows the business to mitigate the risk of entering a failed market and increases the time taken to profitability. Deploy resources to areas where the potential for success is highest, recovery on investment can happen sooner, and scaling is more efficient.
Cross-Sell & Upsell Opportunities
When you sell more to an existing customer, it is one of the easiest ways to increase revenue. Data can show which products or services complement each other and when to present an offer.
For example, an online bookstore may suggest cooking tools and recipe books for those who have purchased a popular cookbook for baking. Businesses can use purchase histories and product affinities to create personalized cross-sell and upsell opportunities for their customers.
AI can recognize lifecycle stages and activate cross-sell and upsell opportunities when you approach a customer’s limits, or when complementary products can offer added value to the customer’s core purchase.
Business Impact: Cross-sells and upsells made during critical touch points can increase average order value, help to improve customer satisfaction by presenting right solutions at the right time and sustaining brand loyalty. The customer believes they are still receiving more value while the business retains higher than normal margins while minimizing acquisition costs.
Risk Management & Fraud Detection
Fraud detection is a critical part of maintaining customer trust and that prevents revenue loss. Utilizing data analytics can expose anomalies in real-time and stop damaging behavior before it continues.
For example, a payment processor may alert on transactions when someone logs in and conducts two transactions from two countries in just a few minutes, or when the spending behavior is suddenly dramatically different. Machine learning models use historical transaction data to update the detection model continuously.
Data sources vary from transaction logs to geolocation data, device IDs, and logins.
Business Impact: The ability to better protect a brand reputation by better preventing fraud, minimizing chargeback costs, and providing assurance to customers of a system that protects their significant commercial investments through contributory factors to brand trust will help to create a more likely repeat purchase business environment.
Optimizing Supply Chain & Inventory
Ineffective inventory management puts a retailer at risk of losing sales or wasting resources. Data provides insights into balancing inventory supply and demand by giving forecasts of future needs by leveraging historical sales, seasonality, and up-to-date sales data.
For example, a fashion retailer could estimate their winter coat demand with a weather prediction in mind and armed with data from previous winter seasons’ sales. With this data, they could order enough stock to support anticipated peaks in demand, but not over-order so they wouldn’t have remainings.
Incorporating lead time estimates from suppliers will only help to build better re-ordering cycles, resulting in lowered pressures on costly inventory misalignment errors.
Business Impact: Better inventory management reduces inventory, lowers delivery costs, reduces out-of-stocks, and keeps customers happy and in stock. When customers can depend on a retailer, they are likely to continue returning, increasing the number of repeat purchases.
Measuring & Improving Customer Lifetime Value (CLV)
Customer lifetime value (CLV) is the revenue one customer generates over the duration of their relationship with a brand. By understanding CLV, businesses can devote resources toward acquiring and retaining higher-value customers.
For example, a skincare brand may find that customers who came in via an influencer campaign have a CLV that is 25% above those acquired through generic advertising. This finding drives a strategic shift in marketing spend to higher-value channels.
Key CLV metrics are purchase frequency, average order value, and retention (also known as churn rate).
Business impact: Spending time and money on customers and channels that deliver higher CLV improves marketing ROI, while also ensuring profits allow for long-term, sustainable growth. Since marketers are always looking for incremental revenue, this will help them as they grow the business while still spending on acquisition!
Conclusion
When business data is collected, analyzed, and acted upon properly, it becomes a source of competitive advantage, business growth, and ultimately it adds value for customers in every interaction. With LocationsCloud, you can easily collect, visualize, and react to geographic and operational business data and can make their decision based on many factors.
Whether it is optimizing campaigns, forecasting future sales, or reviewing locations for future expansion, business action based on insights is what sets the good businesses apart from the great companies. With the proper strategic process and the right tool, business data becomes a leverage point to drive business performance.
