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Analysing and Predicting Consumer Behaviour

Before the advent of the internet, producers used to undertake time-consuming and expensive surveys to understand consumer behaviour.

Consumer behaviour analysis is not a new concept. Data and analytics have simplified the field further. Some companies use social media profiles, search history and past purchase decisions to determine consumer trends, and thus respond appropriately.

What is Consumer Behaviour Prediction?

In most instances, this concept refers to the consumer journey from research to point of sale in the eCommerce industry. Irrespective of the industry in one exists, consumer behaviour prediction aims to get insights on a consumer and harness their purchase decisions to achieve business goals.

While businesses want to promote sales and make profits, putting the customer at the centre of operations accelerates realisation of the set targets.

The Age of Big Data

A common phrase that alludes to data as the new oil.

Google, Twitter, Facebook, and other large multinational companies collect and manipulate data to influence consumers. The morals of such a model are neither here nor there. Plus, this is neither the time nor the place for the discussion which has caused strife among governments, stakeholders, and multinationals.

Ironically, marketers and business owners threw themselves into the game of mining data since it’s the new ‘norm’. Managing website impressions, email marketing, clicks, and consumer reviews provide insights that can be interpreted using modern technology to drive the business forward.

The Value of Consumer Behavior Analytics

Prediction and Identification of Market Trends

If a brand wishes to stand out, it must by becoming the market leader. Look at how some companies such as Apple are the trailblazers while the rest play catchup. The smartphone market is cutthroat and incorporating elements such as privacy features and improved user experience elevates a brand from the rest.

The same applies to all businesses. There’s no dual way about it.

Artificial intelligence, machine learning, and data analytics can help predict trends before or immediately after they appear in the market. Facebook, now Meta, faces uncertain times due to stiff competition from TikTok which has taken Gen Z, the main target audience, by storm. Developing products and services in sync with consumer needs allows businesses to remain relevant.

Identify the customer categories

Most likely, customers come from different demographics. Business customers all bear different characteristics. Some are millennials while others belong to the baby boomer generation, and each comes with varying expectations. Segmenting your audience and putting mechanisms to directly meet their needs is paramount.

Build Information Silo

Knowledge is power. We cannot insist enough.

Generic factors such as location and age are inadequate to make sustainable consumer behaviour personalities. Therefore, go for detailed undertakings even when they seem irrelevant. Some consumers consider activities such as environmental conservation and community social responsibility before making the final purchase decisions. Stock such information and tailor products and services to meet client intent.

Predictive Analytics for Consumer Behaviour Using Modelling

Basically, this method utilizes statistical significance in evaluating customers’ historical data and retrieval of possible future actions. Modeling creates an arithmetic construct highlighting shared behaviour across different customer segments, providing hints on how they most probably react to both internal and external stimuli.

Aggregated customer data is used in the creation of customer behaviour models to define areas on which brands need to focus for maximum output. Through customer segmentation, models inform the marketing strategies deployed and further understand factors influencing purchase decisions.

Machine Learning and Consumer Behaviour

The biggest challenge to marketing is the power to influence because despite best efforts to segment customers, in a real sense, no two customers are alike. Think about intrinsic motivation, preferences, dislikes, and human actions. It’s impossible to collect and objectify such data. Machine learning comes to bridge this gap.

Human beings are rational yet erratic. Sometimes, decisions are made without applying any thought process. Machine learning taps into the limitations of human rationality to deduce insights. It exposes behaviour hitherto unseen by the naked eye and offers a foundation to gauge how existing customers could behave.

Machine learning showcases connections, segments and links past historical data to define future consumer behaviour. Aspects such as preferences, satisfaction levels and channels used are exposed. On this site, we’ve stressed the value of data when using machine learning in predictions. Therefore, the larger the amount of data available, the higher the chances of making better consumer behaviour analyses and predictions.

Steps to Predict Consumer Behaviour

Algorithms and data confuse even the smartest. However, for small businesses and medium firms seeking to develop a roadmap for their niche, consumer behaviour can be predicted in the following steps.

Identify Prospects

One-on-one customer interactions are possible in today’s world due to the evolution of marketing strategies. The relevance of these interactions lies in their ability to provide data on customer categorisation.

Companies store customer profiles, and then cluster any new customers based on existing traits. For example, data points such as demographics, geography, and previous purchases remain applicable to larger customers. Projecting these traits to new customer segments brings a paradigm on how to position business operations.

Leveraging modern tools such as Vertaco to capture technical attributes (monetary value, frequency, and recency) plus attention and returns rate enables precise targeting. Consumer behaviour exists within a multivariate framework.

Extract Features

Critically look at these factors:

  • Best customers
  • Why are they your customers?
  • What is their purchase?
  • Why choose you over others?

Understanding these factors highlights your customer patterns and how best to target them for marketing purposes.

Form Models

Apply machine learning to predict future customer actions including those with high purchase intent and the best products. Assign a bandwidth with a scoring board (say 1 – 10) to separate qualified leads and modify frameworks to reach out to the rest.

Look out into the market for complimentary tools to build objective models.

Customize Communication

It all boils down to identifying the customer patterns that are suitable to each customer segment. The marketing teams harness the insights to spin campaigns targeting high, medium and low-intent prospects.

Marketing tip

Most consumers with high purchase intent require affirmation and information rather than conviction to buy. Therefore, use informational blogs and videos to offer value-driven content.

5 Core Reasons why Consumer Behaviour Prediction is Important

  1. Quickly avail products to the market. When it comes to business, playing catch-up is the last thing any proprietor needs.
  2. Encourage loyalty. When a customer feels the products and services meet their needs and preferences, loyalty comes automatically.
  3. Reduce marketing expenditure. Whenever you set the trend, it’s your competitors who spend more for relevancy.
  4. Identify expansion opportunities. At the heart of any predictions, is the need to open new avenues for growth.
  5. Meet customer demand. Festive seasons validate the need to anticipate surges in demand and prepare accordingly.

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