In Marketing, we generate data every second. every Ad we watch, every Video we watch, every page we visit and every click of the mouse we make online contributes to an increasingly large and varied data set.
61% of marketing decision-makers said they struggled to use or integrate the data they needed last year.
Data analytics is the method used to turn this data into valuable insights – insights that are being put to use by various industries all over the world.
Over the last decade or so, marketing has been revolutionized by data analytics, allowing brands to deliver more targeted messaging and measure their return on investments.
Challenges faced in Marketing & Advertising
Every marketer faces different challenges. some find it difficult to satisfy their customers, while others are having trouble finding the right time and channel to target their customer.
Here are some of the challenges faced in Marketing:
Finding the Impact point.
Unable to check Insights
Falling short on Quota
Segmenting and targeting
Lack of Customer Loyalty
Marketing to new customers
Achieve maximum ROI
How Data Analytics plays a role in the same
Data analytics can help your organizations solve these problems. Understanding marketing analytics allows marketers to be more efficient at their jobs and lower wasted marketing budget.
In 2017, many organizations embrace marketing analytics to create value for customers, to make the customer happier, to increase Profitability.
Use of Marketing Analytics can not only cut marketing costs but can also address the following challenges that modern businesses face:
1. Resolve the uncertainty of Marketing Environments
Today, the success of a company mainly depends on its ability to deal with the unpredictable external environment.
The fast-changing economic landscape and shifting of consumer choices must companies to prepare for multiple outcomes.
Advanced marketing analytics can check these inputs and analyze the past results to find predictable factors of an uncertain environment.
Businesses can work on these high-influence predictable factors to innovate and make arrangements that help them mitigate the risk of uncertainty.
2. Lack of Customer Loyalty
Research suggests that 60-80% of the satisfied customers will not buy from your company.
As the saying goes, “Make new friends, But keep the old. One is silver, the other gold.”
The importance of selling to existing customers can’t be stressed enough in a business environment.
Yes, New client acquisition is necessary for the survival of the firm. However, customer retention and loyalty are critical for accelerated and positive revenue growth.
Marketing Analytics analyzes customers behavior and predicts those set of clients who are more likely to buy from you.
3. Marketing to new customers
The continuously changing marketing landscape and intense competition have led marketers to think of creative ways to reach the target audience.
The right marketing analytics can analyzes present and past to make a few predictions.
These predictions may or may not answer all marketing questions straight away.
However, they will enable your sales and marketing teams to act quickly and decisively.
In the process, you not only make significant savings on the marketing budget but also boost the return on marketing investments.
4. Bringing Right Product / Services to Market
Data visualization is a valuable tool that not only appeals to the eye but can be used to tell, inspire and guide businesses based on customer behavior.
By using data visualization to shows which types of customers buy what type of product, teams can hone in on important guiding questions like - What age range buys our product the most? Which site sells most of our products?
By accessing such information you can now bring the right product to right customer.
5. Targeting the Right Customers at Right Time with Right Content
Targeting the right customers at the right moment with the best offer links back to customer segmentation.
This may be the most common marketing application for predictive analytics because its one of the “simplest” and most direct ways to optimize a marketing offer and see a quick turnaround on better ROI.
According to a study by the Aberdeen Group, predictive analytics users are twice as likely to find high-value customers and market the right offer.
Your data set matters, and best practices dictate using historical data on the behavior of existing customers to segment and target and using that same data to create personalized messages.
6. Qualify and Prioritize Leads
Analytics models like predictive analytics can be used to Quantify and Prioritize leads.
The Following are some of the models
Predictive Scoring: Prioritizing known prospects, leads, and accounts based on their likelihood to take action.
Identification Models: Identifying and acquiring prospects with attributes similar to existing customers.
Automated Segmentation: Segmenting leads for personalized messaging.
All of the above concern qualifying and prioritizing leads, and doing this groundwork prepares teams to apply the strategies that follow.
Sales staff who can prioritize leads most likely to buy (or specific next steps most likely to move the sale forward) will be in a better place to close more often.
7. Analyse Consumer Behavior using Predictive Modeling
Predicting customer behavior and preferences is the hallmark of companies like Amazon and eBay, but the technology is becoming increasingly accessible and relevant for smaller companies as well.
These are some of the models your company can apply to analyse consumer behavior:
Cluster models (segments): Used for customer segmentation; algorithms segment target groups based on numerous variables, everything from demographics to average order total.
Common cluster models include behavioral clustering, product based clustering (also called category-based clustering), and brand-based clustering.
Propensity models (predictions): Used for giving “true” predictions about customer behavior.
Common models include predictive lifetime value; the likelihood of engagement; propensity to unsubscribe, convert, buy, and to churn.
Collaborative filtering (recommendations): Used for recommending products, services, and advertisements to customers based on a variety of variables, including past buying behavior.
Common models (like those used by Amazon and Netflix) include up-sell, cross-sell, and next-sell recommendations.
Regression analysis in its various forms is the primary tool that organizations use for predictive analytics. Defined in simple terms, an analyst performs a regression analysis to spot strength of correlations between specific customer variables with the purchase of a particular product; they can then use the “regression coefficients” (i.e. the degree to which each variable affects the purchase behavior) and create a score for likelihood of future purchases.
8. Identify Which Channel is Working best for you
Greater visibility is one of the main benefits of data-driven marketing.
Before the rise of digital marketing and all its associated data, it was difficult for marketing teams to decide which of their efforts, if any, contributed much towards a buy.
By using data, marketers can now track customers along the journey from first interest to last purchase.
9. Identify Valuable opportunities
Successful discovery requires building a data advantage by pulling in relevant data sets from both within and outside the company.
Relying on the mass analysis of those data, however, is often a recipe for failure. These need to go beyond broad goals such as “increase wallet share” and get down to a level of specificity that is meaningful.
This approach also means moving away from the “usual way of doing things.” Most sales leaders deploy resources, such as, on the basis of the current or historical performance of a given sales region.
Data Analytics can be used here to unlock new opportunities requires looking at data in a new way.
This goldmine of data is a pivot-point moment for marketing and sales leaders. Those who are able to drive above-market growth, though, are the ones who can effectively mine that gold.
The right marketing analytics application presents a holistic picture of your marketing efforts, guides and empowers executives to make decisions that are more likely to succeed.
It tries to solve the critical business problems using data and statistics which otherwise go unnoticed or settled based on gut feelings of a few influential people in the organization.
It also helps customers as well as companies by enabling businesses to find out product refinements that add more value to the buyers.
Applied holistically, marketing analytics allows for better, more successful marketing by enabling you to close the loop as it relates to your marketing efforts and investments.
But, you must be thinking that you have to learn complex data analytics codes and software to do all these and Maybe I’m not up for it.
But do you know you can do all these with Excel? Yes, you read it right, Microsoft Excel has many Data analysis tools like goal seek, scenario planning and more also add-on that can help your Marketing & Advertisement department.
Though many businesses fail to analyse this and invest in complex data analytics tools and don’t get any remarkable results.