Sweet are the promises of personalization. Marketers have a voracious appetite for customer data and know personalization to be a make-or-break factor in CX across all moments, channels, and purchasing stages. According to McKinsey, over

70% of consumers consider personalization a basic expectation and marketers often miss the mark.

That’s where predictive analytics can be a winning move to improve personalization. Read on to find out how you can leverage your business with it.

Why should you care about predictive analytics?

Today, it’s essential to back up the business value of CX through the power of technology. According to Statista, the global revenue of CX personalization and optimization software is expected to cross 9 billion U.S. dollars by the end of 2023.

Predictive analytics is one of the methods that helps create highly personalized marketing campaigns tailored to individual customers by interpreting data in real time and identifying CX issues in customer journeys. Here are some ways your business can benefit from predictive analytics:

1. Analyze customer interactions & understand risks

Customer preferences change quicker than the British weather! Predictive analytics analyzes data (say, contextual data) for data collation to spot such changing priorities. Think of a health e-commerce company that wants to improve its marketing efforts and reduce customer churn.

By using predictive analytics to analyze search query data, it can identify customer intent. For instance, customers searching for “protein powder reviews” are likely to go ahead with a purchase, while customers searching for “What is the difference between whey protein and casein protein?” are still weighing their options. Similarly, it can also identify customers who have searched for keywords like “protein powder refund” in the past 30 days to measure customer churn. Using predictive analytics, the company can segment its customers (such as a high-risk churn group) which can include customers with a maximum number of refunds. It can then redirect its marketing efforts by sending free samples or offering a personalized consultation with a nutritionist to improve their experience.

Predictive analytics also helps companies determine the reasons for customer churn. Based on the reviews or customer feedback (such as pricing, taste of the protein powder, or shipping delays) aka zero-party data, it can develop targeted interventions to address them.

2. Help them with the optimization of marketing campaigns

Using the previous instance, the company can identify the keywords using predictive analytics, such as “best protein powder for muscle gain” that are most likely to lead to conversions and use them to create targeted marketing campaigns. It can also track the number of users who clicked on its emails, visited the landing page, or made a purchase. This information can then be used as part of its hyper-personalized marketing campaigns.

Not only that, the company can use predictive models, such as collaborative filtering, to segment customers and recommend products based on past customer behavior, such as sending targeted emails to each segment.

3. Identify high-value leads and allocate market resources

Using the above instance, the e-commerce company can collect relevant data, such as

Demographics: Age, geographical location, gender, etc.Website behavior: Time spent on the website, products viewed, etc.Purchase history: Amount spent, products purchased, frequency of purchase, etc.

With the help of predictive analytics, the company can find trends and patterns using the above data and establish a scoring system that prioritizes leads with the highest potential, such as hot leads. Using segmentation, the marketing team can allocate resources to the leads who are more likely to convert to paying customers in the next 30 days. It can send early access to new products, personalized emails, and retarget its ads to hot leads.

How can predictive analytics improve customer experience?

Here are 7 ways predictive analytics can improve customer experience:

1. Tapping into customers’ decisions
Companies can identify certain customer behaviors using predictive analytics for their business and goals as part of behavioral segmentation. It can use a mix of both internal (website behavior, purchase history, customer demographics, etc.) and external data (social media, weather conditions, etc.) sources as predictive variables to analyze future behaviour.

Based on the analysis, it can segment the customers based on their behavior and tailor its marketing messages and product recommendations more precisely. Weather-targeted advertising is a great example of how companies can use weather data to predict the impact of seasonality on customer behavior.

For instance, many people prefer outdoor activities like hiking during summer and would look for hiking gear, which coincides with the emerging trend of “summer recreational activities”. Focusing on unique customer needs that a retail business specializing in outdoor recreational gear can find may include segments such as “adventure travel enthusiasts” and “seasonal shoppers”. It can create an exclusive promotion code for the first segment to encourage upgrades to their existing gear and send personalized emails to the second segment about the upcoming camping season and relevant gear.

2. Optimizing marketing strategies

Companies require tools that can make their efforts more targeted and create more successful campaigns by merging all data and providing insights through predictive modeling capabilities. Using the previous instance, the retail business can create a marketing campaign to increase the sales of camping tents. This is possible through the use of predictive analytics to identify customers who are likely to be interested in buying a camping tent by using data like past purchase history, social media engagement, and search engine queries.

By creating a targeted email campaign, it can offer attractive discounts on camping tents for summer, highlighting the needs of individual customers, such as insulated tents, cabin tents, etc.

3. Getting the message right

By understanding customer behavior, a company can predict what products will be in demand in the future ahead of its competitors. This helps in serving the right message to the right audience, say, by combining dynamic creative optimization (DCO) with predictive analytics to create personalized and relevant ads based on real-time data. A company can start by creating buyer personas using predictive analytics to segment buyers more granularly and determine characteristics of those personas to include in the segment filters. This helps create messaging that aligns with each segment at each stage in the buyer’s journey and reflects its brand’s values, style, and voice.

For instance, a healthcare startup wanting to cater to expectant parents can create user personas like “working parents”, “single parents”, “first-time parents”, etc. It can then work on creating personalized content and messaging for each user persona. First-time parents may receive messages related to compassion, family-centered care, expert guidance, and safety.

4. Finding new customers

One of the key challenges for most companies is customer acquisition. Predictive analytics can help brands identify valuable prospects along with the potential value of each customer by analyzing their behavior, past purchase data, and average order value.

During the customer acquisition stage, brands can identify trends and patterns of behavior to find responsiveness to focus on customer segmentation and campaign performance measurement. Not only that, they can identify hidden trends and patterns in a customer’s actions to find out customer attrition before it even happens!

5. Replenish, replenish, replenish

Building an optimal inventory management strategy to avoid over-stocks and out-of-stocks can be tricky. Predictive analytics helps in analyzing past sales and anticipating future demand for better demand forecast accuracy, allowing for better replenishment of inventory. Based on customer behavior and inventory management history, it can anticipate trends to reduce the risk of stockouts. Businesses can easily identify customer segments and demographic data to update existing pricing strategies and create new product lines to meet market demands.

6. Improved customer support

By analyzing various data sources (such as interactions through surveys, forums, subscription lists, CRM data, etc.), predictive analytics can help identify potential issues in customer support, such as service disruptions, product defects, or shipping delays. This information can help route customers to the appropriate support channel. Predictive analytics also identifies at-risk customers by analyzing data, such as their purchase history, support tickets, and survey feedback.

For instance, an electronics retailer can use predictive analytics to analyze negative reviews and complaints about a specific product line to identify at-risk customers and route them to support channels through personalized solutions, such as product warranty extensions or express shipping upgrades on their next orders.

7. Better content distribution

Marketing teams can use predictive analytics for personalized content creation and distribution by finding relevant types of content and channels for various leads, while also predicting the likelihood of engagement with specific content. They can also optimize content distribution channels by identifying the right channels for reaching the target audience to redirect content distribution efforts on those channels.

For instance, a film production company can use predictive analytics to find that horror film trailers work well with late-night viewers. They can schedule their trailers after midnight for better anticipation of their horror films.

Conclusion

To summarize, predictive analytics helps forecast probable outcomes with high precision using data modeling where the data is gathered from various data sources and analyzed to reveal outliers, key indicators, and patterns. It integrates metrics, marketing efforts, and business results with advanced strategies for better CX across the customer life cycle.

ConvertML is a one-stop-solution for all your marketing and data woes. With ConvertML, you can integrate survey data along with hundreds of other sources into datasets seamlessly to understand customer behavior, preferences, and needs. What’s more, you can also prevent customer churn with winning customer retention strategies for at-risk customers. Reach out to us to know more.