Factors That Influence Customer Behavior


Customer behavior refers to an individual’s buying habits, including social trends, frequency patterns, and background factors influencing their decision to buy something. Businesses study customer behavior to understand their target audience and create more-enticing products and service offers.

Customer behavior doesn’t describe who is shopping in your stores but how they’re shopping in your stores. It reviews factors like shopping frequency, product preferences, and how your marketing, sales, and service offers are perceived. Understanding these details helps businesses communicate with customers in a productive and delightful way. 

There are three factors that influence customer behavior: personal, psychological, and social. Let’s dive into each type. 

Unmask customer behaviour to Augment customer satisfaction

Be insightful about your customers to diminish the drop-offs and grow your business with a sophisticated and intelligent customer behaviour analytics.

Collectunerring and dependable customer data to unveil customer behaviour and Understand your customers to create a better experience with customer behaviour analytics.

Well-heeled customer data:

Integrating every single aspect  of customer data from varied sources to create an all-encompassing view of customers and building an interspersed view of the customers across their transactions, interactions, feedbacksetc.

 Dividing your customers into segments up based on common characteristics:

  • Slicing and dicing of customer data to build dynamic segments of previousand existing customers to remit personalized offers, revamping the success rate. 
  • Automation of Intelligent cloud workflow management and scheduling methodfor fragmentation of customers in real-time as they engage with a company.

Customer acquisition analysis:

Fresh look into new possibilities and new customers, dividing them into segments.Checking these segmentsto identify which segment of customers are more likely to respond positively to the acquisition offers.

Important steps to optimise customer acquisition:

-Improve conversions via the website

-Turn existing customers into brand ambassadors

Predicting Churn and Retention:

-Adjust and try a new customer acquisition strategy

-Leverage traditional and non-traditional data to build ML models to detect inactivity and predict potential churn. 

-Build models to identify and understand drivers of churn. 

-Develop models to analyse the efficacy of various churn and reactivation offers or campaigns. 

Measure and iterate

As you roll out new initiatives, use your analysis to hypothesize what customers will think and do in response to the change. Compare their reactions with your predictions, and use the metrics you set out in step 1 to measure the success of the adjustments you made.  

If the first iteration doesn’t provide the desired results, don’t give up! Try another variation or tweak your approach and test, test, test! As we mentioned earlier, behavioral analysis is an ongoing process–you’ll continue to learn and improve as you go. Continuous analysis allows you to make incremental improvements to existing processes and keep a pulse on ever-changing customer needs, market trends, and how outside factors (like a political election) may influence consumer opinions, motivations, and behaviors. 

Advanced behavioral analytics tools like Scuba can help you analyze, visualize, and manipulate your data quickly, without the help of data scientists or advanced coding knowledge. You can create new queries on the fly, change or create new customer segments, and adjust existing queries easily. 

Customer Engagement

What is Customer Engagement?

Customer engagement, a term that has gained a lot of traction among online firms lately, is a measurement of how frequently a brand interacts with its customers during its whole lifecycle. Customers may help brands develop and strengthen a “human-to-human” connection with them and contribute value outside of purely transactional partnerships by consistently engaging with them across a range of media.

Customer interaction inevitably takes center stage as more companies choose the customer-centric approach when creating their marketing strategy to create top-of-mind awareness and accomplish the desired inbound growth.

Understanding Engagement Obstacles

For a company to achieve success, it must close the gap between customer experiencesand expectations. A lot of companies speak of being customer-centric but fail to implement practices that satisfy their customers. The best customer service is one that is effortless and offers a personal touch. Companies that follow these principles gain a considerable advantage over their competitors who fail to do so.

Here are five engagement obstacles that might prevent a company from achieving true customer service success.

  1. Misunderstand customer expectations:

Do you know what your customers want? Failing to understand the exact expectations might prevent your company from building a customer-centric culture that will eventually win loyalty. A company must take out time to analyze customers’ behavior and determine what they want from you and ask them directly in case of doubts. Ensure that your proposed technology /solution/goods and service is making customers’ lives easier.

Convergytics Solution: Following a methodology to give customers space to express their views, acknowledge their emotional state, understand their viewpoints by paraphrasing, and explain their Pros and Cons.

  • Lack of transparency and clarity:

Unclear company policies may lead to confusion and frustration among customers. Customers will certainly leave your company if vague information costs them money and time. Employees must be well-trained and kept up to date about all policies to relay consistent information. The bottom line here is that customers need to trust you, and clarity is key to earning this trust.

Convergytics Solution: Using clear and concise communications, being radically transparent, allowing two-way dialogue with customers, and not incusing any Hidden Costs during the lifecycle of the project.

  • Weak omnichannel engagement

Every company must employ an omnichannel approach to optimizing customer engagement. For example, your customers might prefer to contact you via call more than email or social media. However, if your service agents are not quick to respond on these channels, you are not meeting customer expectations and likely losing customers. Be prompt on all channels and identify if your company needs more agents.

Convergytics Solution: Understand the impact of user satisfaction across channels on various contact reasons for customer loyalty. Carrying Engagement journey mapping to determine cause & effect between different engagement channels.

  • Failure to deliver as promised

TRUST is everything for better customer engagement. If your company is promising something to its customer, stick with it. Be careful with what you promise, both over-commitment and under-commitment can put your efforts in vain. First, focus on delivering exactly what you have promised, and then make efforts to go out of the box when the opportunity arises.

Convergytics Solution: Anticipating areas prone to breakdown and handling them as needed and keeping the clients updated and informed, analysing the problem, fixing it, and establishing the learning points. A shorter time to resolution is the key.

  • Not using all available feedback and data for improvements

To offer the best service, solicit the feedback of both customers and employees. Project success depends on your ability to ask thoughtful questions and look at every aspect of your service at the micro level. Listen carefully to your customers and engage them in a meaningful way, your company will earn their trust and gain success.

Convergytics Solution: Carrying out Customer satisfaction analysis to deliver actionable insights. Create a dynamic and real-time customer journey activation strategy to enhance the experience

Overcoming the barriers to customer engagement might be challenging, but no doubt it is necessary when companies that improve their CX see increased revenue and report cost savings.

Predictive Analytics – Astrology to AI


Analytics is divided into 3 categories namely, Descriptive, Predictive, and Prescriptive. Predictive Analytics basically deals with analyzing past data to make predictions about the future with the help of statistical modeling. Some of the main use cases of predictive analytics are to predict

  • Demand for products/ services
  • Customer churn
  • Employee attrition
  • Fraudulent transaction
  • Loan defaults
  • Insurance claims
  • Stock market fluctuations

This article tries to address the following points

  • Brief History of Predictive Analytics
  • Present Scenario
  • Process flow of a Predictive Analytics Project
  • Business Cases where Predictive Analytics has revolutionized the market.
  • Concluding Remarks

A Brief History

Now let’s try to understand how predictive analytics came into the picture. As many people would think it is not of the recent past (50-60 years). Yes, it has been used extensively and in a more systematic way after the invention of computers, but history goes a long time ago!

Ancient people believed that celestial objects like Sun, moon, stars, and planets always had an astral effect and would affect people and events on Earth. This made many ancient civilizations like Greek, Babylonia, etc. collect very accurate data about the position of these objects in the sky.

A Greek astronomer Hipparchus way back 2000 years ago commented that “It is imperative to have excellent data to make accurate and reliable astrological predictions or forecasts!”. Many Royal Families had an astrologer in their court whom they would consult before making a crucial decision.

Whether we believe in astrology or not is secondary here but what is important to observe is that it created the first foundations for systematic data collection and later used this data to make predictions about the future in other words Predictive Analytics!

Later we also see one of the first-of-its-kind business applications of predictive analytics and modelling. In the year 1689 Lloyd and company in London, which deals with insurance and re-insurance business used data from past ship voyages to determine/predict the risk for different shipping routes and fixed the premium for re-insurance for shipping companies based on the number assigned to the risk.

Present Scenario

Fast forward to the present day. We live in the age of information; we are generating a staggering 2.5 quintillion bytes of data per day! To process this quantum of data would be impossible for Humans. This is where the beauty of computers and programming comes into the picture. With the increasing processing powers of our computers and many open-source programming languages like Python, R, etc., we can process, analyse, and build state-of-the-art Machine Learning Models that are able to make more and more accurate predictions. We are also trying to mimic the human brain in Artificial Neural Networks, all these modern advances fall under the broad category of Artificial Intelligence (AI). There is so much research going on in this field and so much is yet to be known.

Framework for Predictive Analytics Project:

  1. Problem or Opportunity Identification:

A good analytics project starts with the ability of the organization to define the problem clearly! Domain knowledge and expertise play a crucial role here. This phase is all about asking the right questions.

  • Dataset Identification:

The most crucial part of any predictive analytics model building is data.  Predictive analytics is all about analysing the vast amount of data into trends. This collected data helps us with future predictions and remain ahead of the competition. In addition to data available within the organization, we may have to collect the data from external sources.

  • Data Pre-processing:

Once we have decided on the dataset, it’s time to start pre-processing the dataset to remove outliers, errors, and discrepancies. From past experiences, it is evident that data processing takes a significant proportion of any analytics project cycle time.

  • Model Building:

This is an iterative process that aims to find the best model. Several statistical tools and measures will be used to finalize the model. It is important to split our dataset into multiple training and validation sets to avoid overfitting.

  1. Model Deployment and Communication:

The primary objective of the analytics is to come up with actionable insights that can be deployed. The communication of model output to clients, especially the top management, which is responsible for making systemic changes, plays a crucial role. Innovative data visualization techniques like dashboards come in handy at this stage. Deployment of the model may involve developing software solutions and products.

Few Applications of Predictive Analytics:

  1. Amazon – Uses predictive analytics to recommend products to their customers. It is reported that 35% of Amazon’s sales are achieved through their recommender system.
  2. Netflix – Predicts which movie their customer is likely to watch next. 75% of what customer watch on Netflix is from product recommendations.
  3. Google – Predicted the spread of H1N1 flu using the query terms.
  4. Polyphonic HMI – Predicts whether a song will be a hit using Machine Learning Algorithms. Their product ‘Hit Song Science’ uses mathematical and statistical techniques to predict the success of a song on a scale of 1 to 10.
  5. Capital One Bank – Predicts the most profitable customer. The list goes further but we can clearly see from the above examples how major companies are using predictive analytics to dominate their business and stay ahead of the competition.                                                                                                                                                                                                                                                                                                                                                            

The list goes further but we can clearly see from the above examples how major companies are using predictive analytics to dominate their business and stay ahead of the competition.

Industry Sector                 Sample Problems Data Sources
Manufacturing Supply Chain Analytics Quality and Process Improvement Revenue and Cost Management Warranty Analytics Procurement, sales, and production data Warranty and after-sales service data Commodity price data Manufacturing data Macroeconomic data
Retail Assortment Planning Promotion Planning Demand Forecasting Market Basket Analysis Customer Segmentation Price data Demand data at SKU and category level SKU level sales data with and without promotion Planogram Customer demographics data Point of Sales (PoS) data Loyalty program data
Healthcare Clinical Care Hospitality related data All patient care-related data Hospitality related data Patient feedback data
Service Demand Forecasting Net Promoter Score (NPS) optimization Service Quality Analysis Customer Segmentation Promotion Transactional and feedback data Pricing and demand data Promotional data
Banking and Finance Service Demand Analysis Customer Transaction Analysis Credit Scoring Customer transaction data Loan originating data Credit scoring data
IT and ITES (IT enabled services) Demand for Analytics Services Software Development Cycle Time Customer interaction and market research data Internal product development data

Why Convergytics?

Want to know whether the stars will shine for your business in the coming days? Connect with us and talk to our dedicated team of experts. With almost a decade of presence in the industry and has worked with many esteemed clients across myriad sectors spread across the globe, we sure can point the way to the stars!!