Guest author Shane Barker looks at customer behaviour prediction, the newest buzzword in the marketing industry.
Gathering descriptive customer insights is a thing of the past. Now, businesses need to be more proactive and forecast the next steps of their customers.
An accurate predictive analysis can lead to an on-target marketing strategy, which, in turn, can result in better brand growth.
There is a reason why businesses are investing heavily in customer behavior prediction. This is because customer insights are guiding almost all business processes.
A recent study by Alteryx and AbsolutData found that customer behavior analytics is used for boosting sales/marketing by 69% of marketers. Additionally, 63% of them use it for customer satisfaction and 46% for customer loyalty.
Thus, using customer behavior analytics can help in growing your brand. But before we discuss the topic at hand, let us understand the basics of customer behavior prediction.
What is Customer Behavior Prediction?
A quantitative and qualitative assessment of how customers engage with a brand is called customer behavior analysis. This can be categorized into three types:
- Descriptive analysis: Evaluation of customers’ past interactions with a brand
- Predictive analysis: Prediction of customers’ future course of action
- Prescriptive analysis: Indication of the best recourse for a brand
Customer behavior prediction is another name for predictive analysis. It involves anticipating a customer’s behavior and actions even before they occur. Here are some examples of customer behavior prediction:
- Your grocery-ordering app pings you if you’ve missed adding your favorite cereal to the shopping cart.
- Your telebanking call automatically switches to the language that you always select.
- Your preferred payment mode gets highlighted at checkout time.
Now that you’ve understood what is customer behavior prediction, let’s take a look at how it can help brands.
How Customer Behavior Prediction Helps Brands
If a brand needs to survive and thrive in this ultra-competitive era, it needs to have a good grip around its customers. Customer behavior prediction utilizes the numerous “digital footprints” left by customers to help brands stay a step ahead of their customers.
Check out the main benefits of predictive analysis for brands.
1. Precise Segmentation of Audiences
While CRM systems provide basic information about customers, predictive analysis pulls dynamic data about a customer’s evolving tastes and behaviors.
Numerous variables are weighed to create a fluid customer persona. This persona is more meaningful than an obsolete one, which is based on vague demographics and past transactions.
Equipped with the power to predict, businesses can define the actual “value” of a customer. They can take a quick call on whether it makes sense to invest effort and time to nurture a customer too. Additionally, brands can identify high-value groups that are amenable for upselling and cross-selling.
Intelligent segmentation can also direct brands towards buyer groups that are more willing to share their positive shopping experiences with others. With little nurturing, these spenders can become brand promoters and advocates. They can help create a positive brand image and bring in new customers.
2. Personalized Marketing Experiences
Not all customers have the same expectations from a brand. If a business understands its customers and the factors impacting their behaviors, they can create customer experiences that are designed to delight. Satisfied customers often perform repeat purchases, so this is a sure-shot way to secure customer loyalty and retention.
Earlier, brands relied on guesswork or assumptions to gather customer intelligence. Then they shifted to vague data sources such as point-of-sale or customer demographics. Today, brands are using advanced channels such as AI, device-generated data, and social media analytics to understand their customers better.
Customer behavior prediction rests on deep learning, which is a subset of AI. Deep learning involves building a layered (or neural) algorithm that can process mammoth databases of variables. When marketers feed variables about past, existing, and potential customers into the deep learning model, they get a near-perfect prediction about the tastes and motives of each customer.
Armed with this AI-powered knowledge, brands can come up with content, products, and other offerings that are tailored to meet the expectations of each target group, and convert them faster. Platforms like Cortex, Hootsuite, and Zoho Social have AI-enabled features that can optimize your content according to audience needs and preferences.
For example, Cortex analyzes industry trends and keeps you updated with your industry’s visual language. It also discovers visual themes, colors, features, and composition that works best for your audience.
To illustrate this point better, let us consider the example of Netflix. The OTT megabrand has a robust AI-based content recommendation system. Netflix claims that its algorithm is so strong that it influences 80% of the total content consumed by its subscribers and saves more than one billion USD yearly in value from customer retention.
3. Focused Messaging
A spray-and-pray messaging approach doesn’t work anymore. Sending bulk SMS and emails to the entire contact list just produces high costs with little returns.
A brand that keeps a close watch on their customers’ buying patterns gets a fair idea about their next steps and can send event-triggered messages. Such automated text messages require minimal effort, and you can get conversions through them.
For instance, let’s say your website has numerous regular visitors who love browsing through your product catalogs but don’t buy anything. Through customer behavior prediction, you can identify this visitor subset and know exactly at which point they will drop off the sales funnel. You can accordingly make the necessary changes required to get them to become paying customers.
Image via Apigee Insights
Brands can draw maximum mileage from customer insights by fortifying SMS marketing with email marketing. Following up a triggered SMS with a series of well-timed and compelling emails can bring down cart abandonment rates and re-engage lost website visitors.
Studying customer behavior indicates the most active times of audiences and the email subject lines that have the highest success rate. Marketers can use this information to optimize their email marketing strategy.
Conversion rate optimization is another effective tactic that you can use to expedite conversions through focused messaging.
Limited-time voucher codes and countdown timers help create a sense of urgency in your website visitors and speed up their conversion. Here again, predictive analysis of a visitor’s response can help you offer the most engaging deals.
In a saturated market ecosystem, brands will need to outthink each other in order to sustain. A brand that has a pulse on its customers can outperform competitors by a wide margin.
Such a business can strategically plan its next marketing move and product line. This can also mitigate business risks to a large extent and help you grow your brand.
About the Author
He is specialized in sales funnels, targeted traffic and website conversions. He has consulted with Fortune 500 companies, Influencers with digital products, and a number of A-List celebrities.
The 2021 Ecommerce Stats & Trends Report
The latest data on ecommerce trends and online customer behaviour
Graham Charlton is Editor in Chief at SaleCycle. He's been covering ecommerce and digital marketing for more than a decade, having previously written reports and articles for Econsultancy. ClickZ, Search Engine Watch and more.