Whether your business caters to regular consumers, businesses, or public institutions, leveraging hard data offers you a more efficient yet better personalized way to interact with customers. With proper data handling and analysis, your enterprise can gain deep insights into customer behavior and needs, enabling them to more easily provide satisfying bespoke experiences that encourage continued loyalty.
Regardless, many businesses try to use customer data to drive sales but not all of them succeed. Using your customer data to provide consistently meaningful gains will require you to take a systematic approach to data handling, which may involve reimagining how you’re currently running your business.
Let’s examine some basic ideas that can help underpin your emerging customer personalization strategy:
1. Collect Accurate but Comprehensive Customer Data
To start, always gather accurate data from multiple sources. Make it a point to draw from your transaction records, social media interactions, website visits, and completed customer feedback. Your resulting data can include structured data (such as purchase histories and website analytics) and unstructured data (like social media posts and customer emails). The more diverse and comprehensive the data, the more insightful the analysis can be.
2. Ensure Data Quality at the Source
Many businesses fail to leverage data because the way that they gather it at the source is flawed. Though you want to be comprehensive, the accuracy of your data is far more important.
To ensure a clean data set, consider how customers are interacting with your data collection method. For example, data derived from interviews can become less accurate if the collection process runs too long. Similarly, data collected from paid and unpaid surveys may need to be viewed differently because of the differing biases inherent to each method. Even the accuracy of data gained through objective collection methods like site analytics can be affected by a website’s browsing experience.
In any case, take the time to consistently preprocess your data to account for the disadvantages of the method and to correct inconsistencies.
3. Invest in the Right Talent and Technology
To effectively leverage data science, you have to invest in the right technology and, more importantly, people who can effectively use that technology. Advanced analytics tools, machine learning algorithms, and customer data platforms are all complicated investments, and you cannot maximize these without skilled data scientists and analysts at your side. You want to avoid the trap of simply throwing money at technology without considering the human-side investments that have to be made.
4. Simplify Your Understanding with Exploratory Data Analysis (EDA)
Before diving into complex models, use EDA to understand the basic structure of your data. Visualizations like histograms, scatter plots, and heatmaps can reveal patterns and trends that guide further analysis and future approaches to data handling and collection. Using EDA may also help in identifying key relationships within the data if you have difficulty grasping the abstractions.
5. Segment Your Customers
If warranted, consider using clustering algorithms to segment customers into groups based on similar characteristics. Segmentation allows you to tailor marketing strategies so that you can better target the most profitable segments or effectively address multiple segments all at once.
Traditionally, demographics are used for segmentation, but you can also use specific buying behavior and engagement levels for a more refined approach. Again, you’ll want to make sure that you’re drawing from an accurate data pool so that you can avoid making false conclusions later on.
6. Optimize Inventory and Demand Forecasting
Balancing inventory and customer demand has historically been a very involved and often risky process. As such, it’s often accepted that some waste and opportunity costs through overstock and understock situations are inevitable. However, by drawing from purchase histories, these scenarios can be reduced to a minimum, freeing up your cash flow and avoiding unnecessary risks to your business.
7. Implement Predictive Analytics
Using predictive analytics can help simplify work for your analysts by removing a lot of the work involved in predicting future customer behavior. Techniques like regression analysis and machine learning models can forecast key trends such as customer churn, product demand, and customer lifetime value for each potential action, lowering your relative market risks. With proper data hygiene, well-executed predictive analysis will enable you to consistently execute proactive strategies to retain customers and optimize inventory.
8. Develop Automated Recommender Systems
Recommender systems use algorithms to suggest products or services based on a customer’s past behavior. Your data managers can set up collaborative filtering or content-based filtering techniques into customer-facing systems (i.e. product carousels, websites, programmatic outdoor ads, etc.) to provide personalized recommendations for each individual contacting your brand. The effect of this is to ensure more ads coming customers’ way are relevant, increasing baseline sales numbers while also reducing the costs of facilitating each transaction.
9. Optimize Your Customer Service
Analyzing customer queries and feedback helps in identifying common issues and improving service processes. Additionally, the same systems that enable custom-matched product recommendations can also enable highly refined personalized customer interactions. What’s more, chatbots can draw from this data to preempt customer questions and provide a level of personalization that’s close to what a skilled human customer service representative can provide.
It’s Time to Take Your Data Seriously
At this point, most businesses probably understand the potential of data to give them a competitive edge. The problem, however, is implementing data science advancements in a way that provides consistent, meaningful, and actionable results. Thankfully, a new breed of specialized data science firms is now helping businesses use the data they have to better meet their objectives.
Regardless of whether you choose to do data analysis and automation in-house or through an agency, it is clear that data literacy is already a key part of gaining long-term business success. As technology continues to advance and democratize, it will also become a requirement even for the most humble enterprises.
