What Is The Role of Predictive Analytics In Shaping Consumer Behaviour?

Evolving market trends, technology and challenging must-haves have affected consumer behaviour. And thanks to M-commerce, they are spoilt for choices which explains why their buying behaviour keeps flickering.

Today, the stakes are even higher and businesses cannot take risks with the conventional way of conducting market research for new product ideation.

Even if a consumer likes a product and adds it to the cart, they are distracted and lured by lucrative deals that offer better value alternatives for the same or lesser price. Losing potential customers is disheartening, but not despairing. This is where data analytics comes to play.

Data analytics is trusted by digital and business savvy marketers around the globe to study and understand customers and their behaviour. Digital overexposure demands an in-depth analysis of the user’s preferences, behaviour and purchasing pattern. This calls for a marketing strategy that traces the digital footprints of prospective buyers using intelligent tools fuelled by data science. Historical data lets you peek into the past, even though you can’t undo it. You can however, leverage prediction to adapt to dynamic shifts. Analytical practitioners are spoilt for choice when it comes to analytical techniques, which include:

  1. Descriptive Analytics – A basic technique which involves preparing data for subsequent analysis.
  2. Predictive Analytics – Advanced Models to predict and forecast consumer behaviour.
  3. Prescriptive Analytics – Machine learning algorithms for interpretations and recommendations.

In this article we shall focus on Predictive analytics, a category of data analytics that lets you identify the flaws in your strategy, and let strategists implement corrective actions accordingly. So, you can try to permute and combine trials, errors or retry and triumph!

Predictive Analytics

What Does Predictive Analytics Mean?

Predictive analytics is the science of using data, statistical algorithms and AI techniques to deduce meaningful conclusions that can be used to predict the future.

Before understanding how predictive analytics helps study consumer analysis let us first understand its importance.

Consumer analysis lets market research professionals determine the wants and needs of their potential buyers. These steps are crucial for consumer behavior analysis:

  • Discover Insight: Segmenting customer database to identify consumer segments.
  • Attract and Retain Potential Customers: Targeting the segment of customers with pertinent offers by analyzing their profile and past purchases.
  • Leverage Customer Retention: Businesses evaluate customer value and use a proactive approach to retain customers.

Here are a few ways of how predictive analytics helps study consumer behavior:

1) Market segmentation:

The first step in consumer analysis is creating market segmentation which involves breaking the market into various subgroups having similar demographics, behaviors, and attitudes. Using this data you can target each segment individually and cater to their demands precisely. Segmentation involves 3 phases:

  • Affinity analysis is the process of clustering customer databases revolving around common attributes to enable precise targeting.
  • Response model takes a peek at your customer stimulus history and whether it was converted or not to predict the likelihood of devised strategy.
  • Churn analysis also known as the attrition rate will calculate the percentage of customers lost and consequently, the opportunity cost or potential revenue loss incurred.

Data plays a crucial role in developing and deciding the most effective positioning for each marketing segment. Predictive analytics will help you identify the lucrative segments and target them accordingly based on the purchase history. This data is used by marketing managers for optimum resource allocation to reach the most profitable segments.

2) Forecasting and demand pricing:

Demand pricing is the process of pricing products and services based on demand elasticity differences between consumer segments. Predictive analytics is primarily used for creating demand forecast models that predict your business’s sales and revenue to determine the right price at the right time. You can also design experiments to uncover the factors affecting the influence of price on demand to develop favorable pricing strategies.

Predictive analytics will help you amalgamate company information with promotional events, economic indicators, weather changes, etc., that directly impact customer preferences and buying decisions. Subsequently, it identifies new opportunities and initiates more granular insights into future demand.

More recently, demand sensing concept that deploys AI and machine learning to capture fluctuations in purchase behavior in real-time. Some experts perceive it as a method of adjusting predictions and not a standalone forecasting method.

3) Marketing campaigns:

We all remember learning mathematical theorems that had a hypothesis and a resultant stating a hence proved right or wrong. Predictive analytics works like that theorem where data science can be used to identify which customer segments and the audience will be effective to reach and develop actionable insights.

Accurate reporting can exactly tell you whether a campaign was successful and make amendments where it may fall short. This lays the spadework for best practices of strategies to follow, not just in marketing and sales, but making business decisions as well.

4) Predicting customer behavior:

You can deploy predictive analytics to scrutinize similarities and patterns between data variables and likewise, predict behaviors of existing and new customers. Data accurately predict your customer’s next move and also track drop-offs where there is a possibility of losing a potential customer to a competitor. Mapping these patterns will give you insights into campaign outcomes. This will help identify potential leads and prioritize only the ones most likely to convert.

By anticipating customer behavior you can devise effective marketing strategies. Hence, it comes to no surprise that predictive analytics will help in understanding your customers so that you can reach them via the right marketing channels.

Predicting customer behavior

5) Customize content:

The rising trend of a customer-centric approach has prompted businesses across the globe to realize the significance of personalization. But, creating personalized messages becomes challenging due to the lack of accurate and sufficient data and detailed insights. To be able to create personalized content for your customers you have to leverage machine learning, data science, and data analytics to automate segmentation.

The ability to predict customer behavior using data analytics and building models enables you to personalize your content to target those specific leads. Targeting the right audience at the right time will lead to a sure shot way to ROI. Historical data will come handy in creating customized messages to cross-sell, upsell, or recommend products to your customers. Besides that, demographics will provide insights about the choice of the local population to help you understand what offers will lure them to your store. Purchase history can also be looked into to alter promotions based on individual preferences.

6) Power of geofencing:

Geofencing has taken mobile marketing to the next level by empowering businesses to advertise to potential customers within a certain radius of a location. From interactive shopping lists to limited offers on your favorite brand, home security to restaurant suggestions in your area, geofencing has bridged the gap between marketers and consumers.

Geofencing uses technologies based on predictive data such as Global Positioning System or GPS and Radio Frequency identifiers like Bluetooth and Beacon technology to build a virtual boundary around a business location. GPS helps in triangulating the customer’s location accurately while Beacon technology sends alerts when a customer enters or exits a location. Bluetooth technology can tell when you’re in proximity to a beacon such as a checkout counter in a store. Your online efforts will not pay off if you don’t seize chances to survey your customers. Bring geofencing promotion in and doors to various metrics like how often they visit your store, how long do they stay, their purchases, etc. are open.

7) Decision making and reporting:

It’s futile to use data analytics if you can’t reflect it on ROI. The segmentation methods that we have covered in this article earlier like affinity analysis, response modelling, and churn analysis can be adopted to create accurate reports on the customer’s online as well as offline transactions to determine what content you should deliver. Data analytics enables businesses to make customer-oriented marketing decisions.

Data visualization, the process of using statistics and data to build consumer patterns and draw conclusions about a theorem or prove a hypothesis that fosters decision making in the organization can be deployed.

Predictive analytics enables managers to understand the dynamics of their business, foresee market shifts, and cope with risks. Businesses are now embracing analytics and statistical reasoning for making critical decisions about maintaining inventory, hiring talent, managing pricing solutions, etc. This improves efficiency, maximizes profits, and leverages risk management.

8) Boost personalized recommendation:

Competing in a customer-centric world today, simply understanding “who” your customers are is not enough. Instead, focusing on “what they do” and using insights revealed through their behaviour will give a clear picture of your customers’ wants and needs and the best way and appropriate time to deliver it to them. That’s exactly what companies like Amazon and Netflix are adopting. We can’t help but notice that these high customer-centric brands have judiciously used personal recommendations.

However, businesses must also know where to draw the line. Concerns over sensitive information being leaked or stored without consent will result in customers voluntarily opting out of your services. The good news is, analytics algorithms can also tell you if your actions are invasive, or useful. The quest for creating personalized recommendations can sometimes push marketers too far and creep out customers who perceive being digitally stalked. Like for example, the sponsored ads that crop up suspiciously on Facebook and Instagram suggesting slashed prices on flight tickets just minutes after you were mindlessly searching them online. This is where predictive analytics can be deployed to deliver value with a gentle nudge than an obvious push.

Predictive analytics designed their advanced recommendation algorithms to serve their customers personalized content and suggestions based on an individual’s past behaviour. Statistics reveal that 75% of Netflix viewership is driven by recommendation engines and they save $1 Billion per annum through reduced churn. Amazon, on the other hand, generates 35% sales via recommendation alone. These digital titans have used behaviour data analytics to improve customer satisfaction and deliver real business value. Which explains why your Netflix profile suggests movies based on your recently watched list and Amazon sends product notifications and best deals based on your search history, including recommendations that would complement your searched product.

9) Leverage customer satisfaction:

Business in 2020 advocates serving to your customers instead of selling and pulling out money from their pockets. Studies have shown that engaging a new customer is 5 times more expensive than retaining an old one. Customer satisfaction plays a crucial role in customer loyalty and retention. So, for better business prospects, you need happy customers. Predictive analytics plays a crucial role in customer retention along with tools like conjoint analysis will enable you to pinpoint which product or service can elevate customer satisfaction considerably.

Loyalty programs and membership cards not only encourage existing customers to become frequent visitors but also attract new ones to become repeat customers. Loyalty programs could be used in various forms like referring a friend, rewards for making a switch, brand pairings, joining a community, shopping at partners benefits, etc. Starting a business is just not about making your customers happy during the first sale. It is to entice them so they keep coming back to buy more which drives revenue and issues the priceless word of mouth referrals to friends and co-workers.

Over to you:

Predictive analytics can’t be rolled out in a snap. It’s challenging-to-adapt, yet a powerful task that any business can manage as long as they can stay committed to the right approach and are willing to invest in the necessary resources to get the project moving. It is wise to start with a small-scale pilot project in a critical business area to capitalize on start-up costs while cutting down the time before you start reaping the rewards. Once the model is put into action, it usually requires little upkeep as it continues to churn out actionable insights for many years to come. Driving analytical transformations will empower businesses with a competitive edge and stay at the forefront of digital disruption. To sum up, predictive analytics is a robust technique which if embedded seamlessly with the right marketing strategies can correctly predict consumer behaviour and maximize ROI.

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