When Progressive launched its mobile app, it was only used to get quotes for its various insurance products. However, when they looked into the data and studied how their users interacted with the app, they found out that a vast majority were interested in buying insurance directly from the app. Therefore, a "buy" feature was added, which accounted for $2 billion in written premiums within a year. This is the power of data analytics. Let’s have a look into marketing evolution and how does data analytics help increase sales.
EVOLUTION IN MARKETING
Marketing is the process of getting people interested in your company’s product or service. Marketing pertains to all aspects of a business, including product development, distribution methods, sales, and advertising. Prior to the emergence of digital technology, marketing efforts were limited to a relatively small mix of print and broadcast channels. The strategy consisted of basic marketing mix elements namely product, price, place, and promotion strategies, and these were thought sufficient to persuade consumers to buy products. The main marketing communication channels were traditional media so, marketing communication with customers was unidirectional.
Over the years as buying power increased, web technologies altered communication. On account of plenty of similar goods in the market and the facilitation of internet technologies to access information, consumers started to search, question, and compare the products before they gave a buying decision. Businesses had to change their marketing strategies as customer-oriented marketing. Businesses not only used traditional media but also, started to use the internet for communication and promotion activities. As technology progressed, environmental and social factors emerged as a result of intense industrialization and globalization sensitized customers in terms of social issues. Social media altered customer behavior radically. Businesses had to dive into the soul of the consumer getting to know their needs and buying behavior. Businesses started making use of social media for different marketing purposes such as gathering consumer data, establishing marketing communication with consumers, and operating promotion activities.
Further, as time progressed, with rising globalization and an increase in competition, marketing strategies transformed into digitalization. The consumer became more aware of products and services available around and also with the advancement in technology the number of marketing channels increased manifold. Consumer insights, data management, and advanced analytics are key factors of Marketing in order to forecast future trends.
This article will look into what would be the downside of not analyzing marketing data, the reason for analyzing data, and various analytics solutions to help make better marketing decisions.
PROBLEMS WITH NOT USING DATA ANALYSIS
Analyzing the scenario in the past few years, with an increase in information technology, social media, and communication technologies consumer has become more powerful than ever. Consumers are always on some kind of social media and are aware of thousands of products available on the market. Also, the seller needs to market and advertise on a great number of channels now compared to before to reach a vast pool of audience. How to reach to the target audience via different marketing channels available and design creative campaigns to gain consumer attention is the new face of marketing today. Reaching out to these huge audiences on different mediums requires a lot more targeting money and human creativeness. These marketing campaigns produce a lot of data giving information about how many people saw the advertisement and how many converted. If this big data is not analyzed companies will keep on spending with a wild guess on investing in marketing channels and never be able to gauge what channels are most effective or get a sense of consumer behavior. Let us say a company invests 50% of its marketing budget on Facebook ads and the remaining on Instagram ads. They get good conversion rates after a campaign and continue to spend their budget in a similar manner. It is possible that if they analyze user behavior then it concludes that Facebook ads were more effective than Instagram. Then wouldn’t it be a good idea to invest more on Facebook than Instagram? This calls for a crucial need to use data analysis in marketing.
Customer Journey Analysis
The customer journey represents the steps customers go through from the moment they become aware of a particular business or product until they become loyal customers. Usually, a consumer goes through 3 stages in their journey:
- Suspects: these are consumers who are casual visitors and, in some cases, email subscribers. They’re not yet customers but could be interested in your offers, and their purchase intent is low.
- Prospects: These are subscribers that are enjoying their first experience with your business. They’re not yet loyal or long-term customers or committed to becoming customers, but people who are interested in your business.
- Loyal Consumers: these are people who’ve become customers and could become loyal ones.
It is very important for businesses to keep consumers updated with their products and services and make sure they move down the funnel to being loyal customers. Businesses and their marketing teams, therefore, need to perform persona modeling. Personas are fictional characters, which are created based upon some research in order to represent the different user types that might use a service, product, site, or brand in a similar way. Creating personas will help you to understand your users’ needs, experiences, behaviors, and goals. When businesses create these personas, they can analyze the expectations and similarities between groups of people and market products to them in a specific way to gain their attention and possibly lead to selling the product. Personas can even help in designing products and services as per consumer needs.
The process of getting data from your personas comes mostly from qualitative research such as email, phone, or in-person interviews. Talking to customers can provide valuable information into their buying habits, what motivates them, and the words they use to describe your product or service. Other techniques to get user information and product effectiveness is surveys. similar to interviews, they can help you understand if your site or products on your site are meeting their needs.
A/B testing (also known as split testing) is the process of comparing two versions of a web page, email, or other marketing assets and measuring the difference in performance.
To understand how A/B testing works, imagine that we have 2 different designs for a landing page and the business team wants to know which one will perform better. The marketing team creates 2 groups where they give one landing page to one group and send the other version to the second group. After this, the business team monitors how each page performs based on the traffic, clicks, and conversions recorded on respective pages. Once the team has the page that performs well over the other, they can start digging into the effectiveness of one over the other and also what elements on one page are performing better. The whole process of finding what to test, developing a testing hypothesis, and developing the actual test requires data, which you can collect from the company’s analytics provider, surveys (web or email), user testing, and user session analysis. A/B testing is a good way to optimize conversion rates since it analyzes what elements best works for the users.
With the advent of technology, the number of channels where a user can be targeted has increased manifold. Marketing teams target consumers on a plethora of channels to widen their reach. Once a consumer buys a product, business teams keep on sending advertisements to them about new products and services that align with consumer’s needs. But the important question is what channels they should choose to target customers on, increasing the chances of them seeing the advertisement and leading to a conversion. This is where attribution comes into play.
Marketing attribution is the practice of evaluating the marketing touchpoints a consumer encounters on their path to purchase. The goal of attribution is to determine which channels and messages had the greatest impact on the decision to convert or take the desired next step. There are several popular attribution models used by marketers today, such as multi-touch attribution, lift studies, time decay, and more. The insights provided by these models into how, where, and when a consumer interacts with brand messages allows marketing teams to alter and customize campaigns to meet the specific desires of individual consumers, thus improving marketing ROI. For example, if a consumer is exposed to a display ad and an email campaign, but only converts after seeing a special promotion in the email, marketers can note that this piece of collateral played a bigger role in driving the sale than the display ad. They can then devote more resources to creating targeted email campaigns. This helps in increasing ROI for the team and also optimizing spends.
Marketing Mix Models
Budget optimization is one of the key decisions to be taken for planning purposes.
Market Mix Modeling (MMM) is a technique that helps in quantifying the impact of several marketing inputs on sales or Market Share. The purpose of using MMM is to understand how much each marketing input contributes to sales, and how much to spend on each marketing input.
MMM helps in quantifying the effectiveness of each marketing input in terms of Return on Investment. In other words, a marketing input with a higher return on investment (ROI) is more effective as a medium than a marketing input with a lower ROI.
MMM uses the Regression technique and the analysis performed through Regression is further used for extracting key information/insights.In MMM, the dependent or the variable at test is sales or market share and the independent variables can be Distribution, price, TV spends, outdoor campaigns spend, newspaper and magazine spends, below the line promotional spending, and Consumer promotions information, etc.
MMM assists marketers in optimizing future spends and maximizing effectiveness. Using the MMM approach, it is established that which mediums are working better than the other ones. Then, budget allocation is done, by shifting money from low ROI mediums to high ROI mediums thus maximizing sales while keeping the budget constant.
Buying Trends have changed over the years. Traditional ways where there used to be more postcard advertising or a sales rep visiting customers is slowly diminishing and social media, TV, and the internet has taken over a load of marketing and advertising. With the introduction of these channels’ companies can reach to customers globally and in a relatively more personalized manner. With huge marketing budgets and increased customer reach out, there is so much big data that the marketing teams get access to. With the introduction in cloud technologies, the infrastructure to store this data also is relatively cheap. Now it becomes very important to analyze the data efficiently and gain insights from them to optimize spends and increase sales.
The analytics solutions stated above are all important and go hand in hand to produce good results. It’s like a pipeline of analysis that goes in conjunction to reach an optimized solution. It’s very important to analyze customer behavior and their journey to actually get them to buy products and retain them as customers. And so is analyzing channels that get users to convert and so important is the website content the user will be accessing. Optimizing sales figures to get better ROI is at the heart of the marketing teams’ agenda. This is the reason why Data analytics in marketing should be of utmost importance and each company should have data experts that can help reach them their conversion goals.