Find out the value of customer behavior prediction and how to implement it in your organization.
· How much will consumers spend on Product A next quarter?
· Or on Product B?
· Or other products?
· Or on nothing at all?
Knowing these things can help you make better-informed business decisions and significantly boost revenue.
You can gain a better perspective on future sales through customer behavior prediction.
By understanding how consumers will behave, you can fine-tune your marketing and overall business operations, making them far more efficient and effective.
This guide explains the value of customer behavior prediction and how to implement it in your organization.
Customer behavior reflects the journey a prospective buyer takes as they become aware of brands, and then research, select, and buy a product or service. Many factors influence customer experience, including:
· Interactions with a brand
· Previous experiences as a customer
· Purchase history
· Age
· Income
· Lifestyle
· Culture
· Media habits
· Education
· Job
· Beliefs
· And more.
Using big data and predictive analytics to analyze past behavior and personal characteristics, you can predict future customer activities. You can then leverage the results to inform your decisions, improving your marketing and related business operations.
To find out what your customers want, you’ll need to combine your operational data (O-data like sales, finance, marketing, human resources, and more) with experience data (X-data like customer satisfaction (CSAT) and net promoter score (NPS)).
Merging X and O data provides a complete understanding of your company. It helps you understand your brand better by revealing the connections between revenue, growth, and human behavior.
When you are able to predict how customers are likely to act, your business will be able to:
· Identify the people most likely to purchase
· Lower customer churn rates
· Increase customer loyalty
· Efficiently meet product demand
· Optimize marketing spend
· Personalize your customer experience
· Improve customer service.
Businesses that leverage predictive analytics to anticipate customer behavior typically earn a high return on their investment in it.
The most useful customer behaviors to understand are:
Churn (attrition) occurs when a customer stops purchasing from your business. You can control churn by using data to determine why people stop doing business and then take steps to eliminate the related issues.
Retention is the number of people returning to do business with you. You can use data to figure out why they do and apply what you learn across your operation.
Satisfaction is how happy your customers are working with you. When you use data to find out why buyers are pleased with your operation, you can expand on these things, increasing the number of satisfied customers. Pleased buyers are likelier to buy more from you, recommend you, and stay loyal.
Engagement is how actively involved consumers are with your company. Tracking engagement with your marketing and communication will help you do more of what different people interact with, which can deepen and increase customer relationships with your brand.
Human behavior is not static. It can be impacted at any time by many things, such as evolutions in social media or economic changes.
You may already use tools to monitor past customer behavior. This is valuable, but it doesn’t reveal how they will behave in the future.
Behavior prediction lets you better understand factors that might influence customers to change their behavior or maintain current or previous patterns. There are several benefits to predicting customer behavior, all of which can help improve sales.
You likely segment your customer base into different groups.
The issue: Most segmentation is based on historical behavior from the records in your customer relationship management (CRM) system.
Predictive analysis adds another layer to your segmentation by accessing dynamic data showing where current behavior will likely go next.
Your buyer personas and segments are likely based on historical data and relatively static demographic information. Adding the predictive angle to personas and segments provides something far less static, indicating what products those in that segment will likely be interested in and purchase.
Predictive analysis can help determine which groups may be high-value and worth investing in marketing time, effort, and dollars.
Taking this further, using better segmentation allows your brand to identify people likely to leave positive reviews and posts and become brand ambassadors. They are definitely worth giving a little extra loving care to.
Consumers are no longer happy with “personalization lite.” They demand fully customized interactions with your brand, or they will move on to competitors who seem to get them better.
Predictive models use artificial-intelligence-based deep learning to better understand the needs and tastes of your customers and their motivations. You can leverage this information to personalize your products and marketing campaigns to align with their preferences and needs.
Predictive analysis essentially brings you closer to your customers.
Personalized experiences, coupled with fundamentals like high-quality products and superior customer service, help drive higher sales, build customer loyalty, and improve retention rates. All this results in the coveted higher average customer lifetime value (CLV).
How effective is your marketing strategy?
Perhaps you advertise and post on multiple social media channels and earn a reasonable number of clicks and conversions. Maybe you earn some repeat business through your email marketing efforts.
Is this the best you can possibly be doing?
Monitoring and analyzing customers’ behavior helps you focus your marketing efforts on top-performing ones. It guides you toward the marketing tactics, messages, and offers customers are most receptive to.
Your efforts aren’t limited to customers. Predictive analytics also helps you generate sales from people who have not yet become buyers. Perhaps they have shown interest in specific content topics or have put products in their basket and abandoned them. Predictive analytics can help you serve up optimal offers and messages that will get them to convert on future visits. It also allows you to determine where people are likely to leave your marketing and sales funnel, so you can adjust your approach by doing things like inserting pop-ups, offers, or messages that address their pain points and move them ahead.
Ultimately, the insights provided by predictive models allow you to focus efforts on the things most likely to generate results.
Here’s what you need to do to leverage predictive analytics to increase sales.
There’s data and there’s quality data.
The data you use for predictive analysis must be accurate and relate to the customer behavior you want to understand and improve. You may leverage data from many sources including your website, CRM, mobile apps, and prospecting tools.
Your predictive analytics data falls into two categories:
1. Qualitative data
Examples of qualitative data include:
Customer feedback from sources like email surveys, information gathered by bots on a call, online ratings, or website forms.
Analytics and insights gathered from interactions between your business and customers.
2. Quantitative data
Sources of quantitative data include:
Previous purchases, including buying patterns and preferences for certain types of products or services.
Browsing history and other digital activities, such as how often customers visit your site, what they view, the time spent on each page, and what they click on.
Social media engagement, including likes, clicks, shares, comments, customer reviews, and online discussions about your brand (captured through social listening tools).
Next, follow these six steps to transform your data into predictive models.
1. Gather relevant customer data.
2. Organize and combine the data into a single dataset.
3. Clean the data to ensure predictions will be as accurate as possible.
4. Add any variables needed for machine learning to understand your data.
5. Select the methodology or algorithm that meets your needs and is appropriate for your dataset.
6. Build your model.
The right algorithms, coupled with machine learning, can identify potential patterns in your customers' future behavior. These can include high or low purchase intent, churn, and preferred products.
View predictive analytics results as similar to lead scoring. Assign a numerical value to each metric, for instance, a score of one to ten. This will help you more easily assess how likely each customer is to purchase something.
You can leverage the insights you gain from predictive analytics to improve the effectiveness of your marketing.
Use factors like purchase likelihood and product preferences to recommend tailored options to your customer segments or feature their wants and needs in your content marketing campaigns. For instance, if you know a particular customer segment is interested in — and likely to buy — pink dresses, you could feature them on your website when they visit, include them in social media posts, and offer up information about them in style blog posts. Repeat exposure will eventually compel them to purchase a pink dress.
You can use predictive analytics to reduce customer churn and retain more of them. You can even go beyond that and leverage it to build brand loyalty.
Customers are more likely to continue to buy from you if you target your marketing to meet their needs and expectations. If they see themselves and their desires reflected in your marketing, they are more likely to buy more things from you and less likely to check out competitors who better reflect them. Going beyond, if they find delight in your marketing and communication, they will be likelier to share what they love about your business with friends and family members, turning them into brand advocates.
Reducing churn is critical for businesses because selling more to existing customers costs far less than bringing in new ones. And brand advocates can help you generate sales with no marketing at all.
Another factor to consider is that targeted email marketing is less likely to irritate people the way meaningless, random efforts can. This can help reduce unsubscribe rates, making it more likely your messages will get through to the right people.
Selling something to someone is valuable. Selling more — and more expensive — items is even better.
Predictive analytics provides the opportunity to boost revenue by upselling or cross-selling other products. It allows you to identify high-value customers who may be interested in increasing their average spend.
Going back to our previous example, an analysis could reveal that customers who purchased a pink dress from you might be interested in buying a second one, or may want to buy a pink jacket or accessories. Featuring these things in your marketing to them will likely get them to purchase additional items.
People contact your customer service department for many reasons, from questions about using your products to complaints about shipping. Predictive analytics combined with other data from your customer relationship management tool can indicate what people expect from these interactions and where your business may be falling short. More importantly, it could show you what good service means to different segments of your customer base.
Excellent customer service may not directly generate revenue. However, it can reduce customer attrition, improve loyalty, and result in future purchases and referrals from loyal customers.
Predicting customer behavior allows you to better understand different customer segments and determine what they are likely to purchase next. In the competitive business landscape, this can give you an edge in marketing and sales success.
Practicing predictive analytics effectively can result in reduced customer churn and increased retention. Leveraging insights into future purchase behavior allows you to develop more targeted marketing and communication campaigns. It can also significantly improve your segmentation and personalization efforts. Taken together, the benefits of practicing predictive analytics can drive more dollars to your bottom line.
Got questions about using predictive analytics to improve your marketing and improve sales. The experts at Jarrah are always available to answer your questions.
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