Businesses that believe that data analysis is one of the critical aspects of making decisions are more likely to be profitable almost 19 times. Also, around 54% of the businesses using marketing analytics tools result in higher profits than average. With so much scope for profitability with an analytic-centric approach, it has been proven to be one of the most integral parts of any business functioning. Not only does analytics help in optimizing a company's performance but also in forecasting future business trends. Before opting for such an approach, you should be well aware of how it will positively impact your business in the long run. This article will comprehensively explain how an analytics-centric approach in your business can lead to higher profitability and increased sales.

What is Data Analytics?

Data analytics involves analyzing and evaluating raw business data and converting it into insightful information that helps a business perform actions more efficiently. Here are the critical areas in a business where data analytics helps improve sales:

• Customer Acquiring Estimation

• Sales Process Enhancement

• Business Goal Setting

• Performance Management

• Sales and Profits Forecasting

• Customer Analysis

• Sales Attribution

Why Should You Have A Data-Centric Approach?

Here are the top reasons to have a data-centric approach in business:

• Provides better user experience and customer service: Customer satisfaction is the key to driving profitability in any business. Data analytics tells you about the audience's demographics, age, interests, needs, concerns, and queries. This also helps in developing ideas for new products and services. You can custom-tailor customers' requirements by analyzing audience data and keeping a close eye on their needs. It further helps generate a higher customer retention rate and builds solid relationships with your customers.  

• Perform operations efficiently: You get a detailed view of your current position by analyzing your company with numbers and stats. It lets you better understand the needs and concerns of your team members and where your system is lacking. This helps efficiently manage the tasks to avoid the mistakes and errors created earlier. 

• Escalates marketing: Business data analytics provides valuable insights into how your content and campaigns are performing. This helps in improving the quality of the campaigns and targeting the right kind of audience. Furthermore, your time spent working on projects that aren't in line with the prospective customers' interest also decreases.

• Improves decision-making: An effective data analytical approach helps an organization make informed decisions, leading to better and more profitable results. Whether it's choosing the kind of content for marketing campaigns or developing a new product that works for the audience, it helps gain insights into all aspects of a business. An analytical approach also eliminates manual tasks and chances of human error.

Methods to Collect Data for Business Analysis

Here are the top ways to gather your business data:

• Interviews: One of the most common data collection methods is talking one-to-one with prospective customers. You can discuss issues and topics related to your niche and ask for customer recommendations and views via webinars, radio shows, phone, email, and podcasts. You can also conduct sampling events and ask prospective customers to try your products. This will provide you with real-time insights into your existing products.

• Surveys: Sending out surveys and questionnaires to get a clear view of customers' experience with your brand is a great way to interact with customers using less effort and resources. The only thing to remember in this method of data collection is to craft multi-directional questions that don't lead them in a single direction. You can conduct in-person, web, and mobile surveys for better customer insights. This includes both qualitative and quantitative data.

• Transaction Tracking: This includes understanding your customers and target marketing efforts better by keeping a close on the monetary transactions they make with your brand. Tracking purchases on your websites and e-commerce platforms is one of the strongest ways to get customer insights based on the products they like.

• Social Media Monitoring: Keeping a check on your company's social media engagement effectively identifies audience needs and expectations from your brand. Collecting customer data using social media posts, stories, and polls from various platforms using third-party online platforms like Hootsuite gives detailed and organized insights about your followers. These tools help you stay updated with the trends and latest hashtags to keep up with the competition.

• Observing: Closely observing how visitors interact with your brand through your website is a great way to collect customer behavior data. Observing customer data manually can be tedious, but if you select an efficient data analytical tool like Finteza, it helps record your customer's journey through your website data in a detailed form through reports that clearly mention the traffic sources, traffic quality, new visitors, session time and fraud traffic detection in real-time. Here is an example of the website performance report that will help you identify the top ways of website conversions to maximize your company's sales and profitability. These kinds of analytical reports also help in better decision-making to improve the overall ROI of the company.

How a Data-Analytic Approach Helped Businesses Derive Profitability?

Here are the top case studies that proved how analytic-centric helped various businesses to increase sales substantially:

1. McKinsey's Study On Unlocking The Power Of Data In Sales

McKinsey started by surveying about 1,000 business organizations. They found out that about 53% of them are performing great in sales due to their robust data analytics. On the other hand, 57% of them are not effective users of advanced analytical tools and struggle to benefit from them. This survey also showed that companies who focused more on analytical areas to create more value and implemented them wisely succeeded more in generating revenues. Here is how McKinsey studied over 1,000 companies analyzing data on the critical areas of a firm:

Lead Generation

Companies combine customers' previous histories with external data, including news reports and social media, to develop a 360-degree view of the consumers. This predicts what are the factors that are most important for lead generation and sales strategy. The survey described an IT company focused on big data analytics to predict prospective customers. They found that their target customers were established firms rather than startups they had focused on earlier. Changing their target towards established businesses helped them increase lead generation by 30%.

Talent and People Management

Companies are including CRM, email, and calendar data to access employee data more clearly. They found in their research that one of the companies used a data-science organization to understand the behavior of the presale professionals that correlated with productivity. Based on the data the outsourcing company acquired, they trained their team to maximize productivity. Using predictive pipeline management, the company's cost of sales was reduced by almost 6%, and revenue increased by 2%.

Customer Lifetime Value

McKinsey discovered that many of the top B2B companies include next-product-to-buy algorithms that recommend similar products to their existing customers. One of the top logistics companies refined historical customer data to identify cross-selling opportunities. This helps in creating tailored micro campaigns around them. The company improved its sales margins by five folds for its key products using this approach. Another research based on a chemical company showed that this approach helped retain the customer base significantly. Their prime focus was to reduce the churn rate; hence, their team built a futuristic business model with 30+ variables and evaluated 10+ key factors that pushed customers away.

This helped them realize that the more purchase options of products a customer had, the less likely they were to leave their company.

Each of their regional sales managers came with a list of at-risk customers. They were guided to make these customers stay with them for a long time. This data analysis and customer insights helped the chemical company reduce its churn rate by 25%.

Price Management

Deal analytics offers price transparency. McKinsey says that decision-tree analytics representatives identify similar customer patterns and comparable deal information to manage prices. In this, consumers with similar pricing patterns are grouped based on various factors, including industry verticals, past experiences, and size. Based on this similar pricing analytic technique, one software company improved its sales on return by more than 20%.

2. How Big Data Analysis helped Increase Walmarts' Overall Sales

Walmart has around 10,900 stores and ten active websites, with more than 245 million customers visiting them daily. Their social media mentions are up to 300,000 per week. Not only are they big on customer data, but their employees are much more than their average customers every day. They have customer data of nearly 145 million Americans, of which around 60% are U.S. adults. They store customers' data based on their living place, what products they like, and what they buy.

With such an extensive data library, it becomes essential for them to analyze this data effectively to remain profitable. They are continuously working on leveraging this big data to improve their operational efficiency. This is one of the key reasons why Walmart became successful.

Here is how Walmart uses its big data to increase its sales margins:

• To improve its customer retention rate, Walmart uses a saving catcher application that alerts its customers whenever the competitor reduces the price of a particular item they have already bought. In this application, customers are compensated with a gift voucher that amounts to the difference value.

• eReceipts application of Walmart secures customers' purchases electronically and generates electronic copies of all their purchases. Also, Walmart provides specialized recommendations by tracking customers' credit card purchases.

• Using Mupd8, Walmart can detect service-related problems a user faces. Based on the customers' activity, this application also computes suggestions and next steps for users.

• They leverage social media data to identify trending topics and products to launch those products across all Walmart stores. Cake Pops, one of the hottest products of Walmart, was launched by a social media trend on cake pops.

• Using a predictive analysis approach, Walmart amended its shipping policy. They increased the minimum shipping amount from $45 to $50 for an online order. In addition to this, they also introduced various new products for a better customer shopping experience.

• Another way Walmart uses its predictive data analysis approach is to determine the number of associate counters needed at a particular store at a particular time. This is done by analyzing the best form of checkout for customers.

• Walmart's Shopycat gift recommendation application recommends gifts for your friends by extracting data from their Facebook profiles. This app increases the scope of its customer reach through the word-of-mouth technique as it also allows users to send messages to their Facebook friends to ask them if they would be interested in buying a gift voucher or a Walmart product.

Using these approaches, Walmart is witnessing repeated sales with an increase of 10% to 15% in online sales and approximately $1 billion in incremental revenue. This has become possible because Walmart analyzes its before and after sales data to change its e-commerce strategy

Conclusion

Moving your company towards an analytics-centric approach is the need of the hour for businesses to sustain in their niches for a long time.

Using the case studies of top companies, this article has comprehensively described how you can identify your target audience, improve your sales processes, and drive higher profitability by implementing the right analytical tools and techniques.

Though taking your business towards an analytic-centric approach is a complex and tedious process, data analytics always comes to help in the right decision-making, irrespective of the industry.