How does fan Analytics help business to generate better results?

Data and Analytics became the vital part of all business. We saw How Big Data and Analytics used for Data Driven Decision making. In this post, we look at “How does fan Analytics help business to generate better results? Before we get into details, it is important to understand a Fan and Fan Ecosystem?

Who is a Fan?

(Fan Definition: Wikipedia) A fan, or fanatic, sometimes also called an ardent follower or supporter, is a person who is enthusiastically devoted to something or somebody, such as a band, a sports team, a genre, a book, a movie or an entertainer. 

Fans express their passion or interest through certain behaviours like social interactions, wish to acquire objects, posting online, etc. So, understanding a fan in detail helps to communicate and engage with him well. 

Fan Ecosystem

Fan ecosystem is an environment that interconnects fans, their interactions, and any activities performed which related to the devotee.

What does really matters in a Fan ecosystem? 

For the success of any business, it is essential to understand their customers or fans voice, feelings and needs. Until brands are able to communicate with their fans by understanding them in detail, the sustainability is very low in the market even if there is a convincing value proposition.

So, how do we understand Fans better? The best practice is to listen to them, engage them and analyse. A Fan ecosystem basically includes three things, they are 

• Fan engagement
• Fan Relationship Management- Analytics
• Fan Activation (Monetization)

Business decision making depends on the results of the important strategy of social media called Engage, Analyze and Activate.

Fan Engagement

Engagement is nothing but Interacting (meaningful communication) with fans. At Present, engagement shrunk to social media updates or posts. Fans are needed to engage through all the mediums or channels they connect with a brand like social, ticketing, email, websites, shopping, direct and etc. 

Mass level engagement became meaningless and won't work anymore; Individualization is the new face of engagement which provides a unique experience to fans. Fan insight helps the customization of experiences by an understanding of likes, dislikes and behaviours.

Fan Relationship Management(FRM)

Fan relationship management is handling fans in individual level with the help of data analytics. As everything connected with digitalism, user leaves a footprint of whatever action they perform like shopping, transactions, search, visits, social media interactions (comment, share, posting.).

There is a clear saying now, "Facebook knows a person better than his/her best friend"

How does it possible? It is by analysing all the activities a person performs on Facebook which makes it easy to predict his behaviours. But Facebook does this analysis only with online data. What about offline activities? Fan relationship management is a solution that built over Fan Analytics, which collect and aggregate fan data from various online and offline and create a 360-degree view of fans with actionable insights to manage him better than ever. 

Fan Activation(Monetization)

        Fan activation is getting fans to do what you want them to do through a series of activation services with the objective of monetization. It is a multi-level process of converting a fan from unknown to an advocate fan. Activation enables brands to monetize fans through insights and predictive analytics.

How does Fan Analytics help in Decision Making Process?

At present, fans are in a disengaged state where most of the brands are depended on social media channels to stay in connected with them and it is done on the page level. Due to limited fan intelligence, brands and teams face hindrance in generating better results.

With the Help of Fan Analytics, brands would able to overcome their pain points and increase ROI.

Fan Analytics provides brands, an in-depth knowledge and 360-degree view of fans at an individual level. It drives better business decisions by effectively leveraging fan data and the resulting insights. 

Let’s now look at how does Fan intelligence helps in BDM,

1.   Higher Engagement Rate across all touch points

       Fan analytics helps brands/teams to understand fans likes, dislikes and behaviours based on their online and offline activities. Engagement is not something which has to be restricted with social media updates like I mentioned earlier. It is really important to engage fans through all the touch points because it increases the fan affinity and loyalty. Analytics helps to engage fans based on their preferences which basically increases the Response and conversion rates.

For example, sending a 10% movie discount coupon code after his purchase which induces him to book another or sending a thank you note directly to the fan by mentioning his name from the hero of that movie, etc.

2.     Loyalty management and rewards

Identification and managing of loyal fans help to increase the brand value and revenue. How do we find our loyal fans? Loyal fans are the not only group of people who continuously interact with your social media pages but also people who support your team endlessly purchasing tickets, watching the match on TV or stadiums, attending offline events, interacting through Fan contests, subscribing to email campaigns, etc.

Fan intelligence helps to find loyal fans by analyzing millions of data from various places and identify various patterns to convert them as advocate fans and increase the brand value. It also helps to activate these fans through cross selling and up selling.

3.      Fan Relationship Management

We know Fans are the biggest asset of any team or brand. Maintaining the relationship with fans are very much important for the success of any business as they are the largest group of revenue drivers and income deciders. FRM identifies the places where a fan is more active irrespective of channels, mediums- online or offline and enable teams/brands to connect with them based on their past and present activities and preferences.

FRM brings online and offline fans to one place and manage relationship. Individual fan insights help to cluster the fans, create and target the campaigns and review the results and increase the ROI.

4.      Independent predictable value and value generation

It is now possible to calculate the individual monetary value a fan generates and predict how much he is valuable he is for the business with help of Fan intelligence. The value is calculated based on past transactions, spending nature, household income level, interest and behaviour. This intelligence helps brands to plan and execute their marketing and advertising campaigns in an optimised way.

With the help of Fan analytics, brands can now create more personalised offers to monetize them better with the higher response and conversion rates.

5. Measurable and Optimized Marketing Management

Technology and digitalism help brands to measure anything that is executed. Most of the times,the measurement and optimisation on

the campaign level. Now it is passed the campaign level and analytics is performed on an individual level with the help of Fan Insights. 

Most of the brands fail to make profit because they fail to gather the advance insights that separate success from the fail.  Marketers and Brands are doing marketing activities based on brand level based on objectives and goals rather than understanding the fans. The goal is to understand the fans by walking into their shoes and generating creative ideas from those learnings and insights to take decisions that eventually take business to profit margin.

6.      Alignment of sponsor brands with fan persona's and demographics

Selling sponsorships or getting a sponsor for an event or team became very hard as there are no specific metrics for proper measurement and sponsorship value can calculate the % of sales gone up during that particular period. However, there are no mechanisms yet to find out whether the sponsor brand aligns with fans affinity. 

For example, let us say 70% of fans of a team “X” are using low-end hand phones and shows interest in mobiles and technology. So, team X can easily sell sponsorship to a mobile brand. Because the market opportunity to sell more mobiles are higher by associating with team “X” rather than sponsoring any other Team.

Fan intelligence enables sponsorship sales based on insights and drives better results. So, it is important to look at the advanced level fan insights from the team fan base like fan affinity, likes/dislikes, age group and gender with household income level, etc.

 

In short, Fan Analytics is a Fan based analytic driven approach that helps teams and brands to take better decisions with the help of advance level of Fan insights. Individual level insights make business people to make more result oriented decisions by not only generating money but also saving the extra cash that spent for marketing, sales or anything as well.

PS: My next blog showcases Real world Analytic implementations that helped sports business to achieve the Goals.

Big Data and Analytics for Better Data Driven Decision Making

Big Data and Analytics are the two most used buzzwords of 21st century. Data Analytics helps to understand things in detail and how something works. Use of analytics help to increase productivity, make better data driven decisions, and gain an advantage on competitors and market activities.

What is Big Data?

Big Data is the most overused and misused buzzword in the digital age. Big Data has various definitions according to the context it represent. For example, in business Big Data means Data Analytics but in technological terms it is a data set that is too big to be processed.

Big Data is a term used to represent extremely large data sets with unstructured, semi-structured and structured data that has the potential to mined for information, trends and patterns, especial relating to human behavior and interactions. 

Why is it Valuable?

Big Data is the next frontier for innovation, competition and productivity. Success depends on measurement and understanding of something and it helps to improve the business and return on investment. 

Data is easily available from various touch points like Social(Facebook,twitter, Instagram..), email campaigns, websites, ticketing, merchandising, mobile app usage, etc; proper measurement and analysis helps to optimize the business performance by generating better financial results. It could be either for Saving the money by optimizing the existing process or making more money by understanding the new opportunities.

Analytics and Big Data

We will start with understanding the difference between Analytics and Big Data.

Form Wikipedia, "Analytics is the discovery, interpretation and communication of meaningful patterns in data". Analytics is built on the foundations of Statistics, Probability, Computer programs and operational research.

Analytics is the method used to find the meaningful information, trend and patterns from the large data sets and it can performed only if data is available. In simple, big data is useless if no proper analytics is performed.

Data can be captured from multiple touch points where a customer is connected with brands. It can then be analysed to make conclusions and patterns for deriving data driven decisions based on business objectives.

What are the Challenges in integrating Analytics into Business Decision Making?

The biggest challenges are setting up strategy, tools and technology and right talent to create business value.

Integration and Implementation of Analytics

Mapping out an analytics plan is difficult by creating the right strategy's based on analytics for business development and decision making. Also, the right people who understand the data and business to generate better results and value. 

What is working or not?

The other biggest challenge to find out what is working or not? Understanding the business in detail is critical for the success. 

It is important to find out what is working or not to improve the business, it depends on marketing, competition, operations, location and decisions etc. Data alone never shows what is working or not, but need analytics to find out how various categories of business can be improved.

Data Scientist is a new job role which is evolved as a result of these challenges. It combines the skill sets of business management, consultant, analyst and a programmer.

How to use Data and Analytics in Business Decision Making?

Big data calls for data driven decision making. Analytics can be used for decision making only if there is clear and present need,question,challenge.Until there is no crisp and clear statement or objective, analytics driven decision making is useless. 

Analytics is used in decision making based on the applicability of Analytics Frame work. Though data analytics used for Decision making process, the right decision has to taken and implement by business analysts or managers because data is just an element that make the BDM process more easier. 

At times, the data driven decisions can go wrong, only through the continuous analysis and understanding of the scenario bring out the better data driven results.

"Data will always helps to improve the performance but data alone never sufficient to show the best outcome in the biggest market"

Big Data and Analytics are the core part of any business decision making process as it helps to understand Needs, opportunities, performance, challenges and results. It can drive better outcomes by using the quantitative and qualitative methods. 

PS: Next blog is about how does "fan analytics" disrupt the business decision making. And, how does the implementation of Fan Analytics in the Sports and Entertainment Industry helped brands monetizing their Fans in a better way.