Top 5 ways to Leverage Data for Growth| Experts’ Roundup
Let’s begin with a simple question. Why do we need to bother with leveraging data for growth? With the bulk of transactions happening online and digital marketplaces booming, a plethora of choices are available to consumers. Customers are inundated with choices, be it for a product or service. Therefore, companies have to be extremely smart and leverage data for growth while designing their products, segmenting their customers and pushing their products in the market
With a stiff competition and a global marketplace to compete in, the one resource that organizations may use freely is data. Data is the omnipresent resource that is now a necessity for organizations to make use of, if they want to stay alive amidst the competition. According to Forbes Insights,
Data is the DNA behind the powerful analytics and insights that are helping modern organizations identify new products, determine how to better serve customers and improve operational efficiencies.
We had come up with an interesting poll in our Facebook Community Growth Folks basis which, we decided to write this blog.
Presented here are some of the top avenues in which organizations can leverage data and ensure unprecedented growth, sustainable in these digital times:
Customer interaction and CX
Why do companies even need to look at the customer experience aspect? The answer’s simple. It provides a definite competitive advantage. Engaging customer experience is the one of the primary reasons for better consumer retention, making higher profits and having a sustainable business model.
How many companies are in a dilemma to create an engaging user experience, business analytics can help in this regard to identify what aspects need to be incorporated to ensure long term customer satisfaction. Companies need to collect, analyze, understand and use, both active and passive customer data to ensure that they make CX better.
Personalization is the keyword when it comes to successful customer interaction. The success rate with personalization is extremely high and leads to higher engagement, conversions and thereby revenues. While looking at mobile marketing, companies have seen for example that consumers need apps that are highly personalized to their tastes and show them results that are keyed in to their preferences.
Hyper successful dating platforms like Tinder and OkCupid use your data- the information you provide- to determine, not just the matches that are being shown to you but also the ads that are displayed when you swipe. The entire business model relies on it and it can be said that they have leveraged data quite successfully. It is their proprietary algorithms that are behind the success of delivering you the highly personalized matches that you are so fond of.
Segmentation helps you analyse and profile your entire target base. This in turn helps you communicate with your target audience better and to connect with the right kind of audience that will implement your service or product. Having an idea of the kind of audience that you need to focus on also helps you cut down on your marketing budget.
A prime example of companies leveraging data for segmentation purposes is Facebook. This giant social network is effectively allowing advertisers to leverage consumer insights generated through Facebook to target audience segments with thousands of different purchasing behaviors.
While dealing with data used for segmentation, the basic requirement is that it must be high-quality data that does not lack basic elements such as age group and geographic location.
Creating a new product line or revenue stream
In this digital age where customer behaviours and trends change by the hour, it is always recommended that brands keep a tab on whether their offering is still relevant to the audience or not. Big data provides the best possible Avenue to gain and use customer feedback. This allows companies to analyse how their offering is being perceived by the customer and whether some change is necessitated.
This also lets the companies know well in advance if a new product line can be launched in accordance to current trends. An example of data being leveraged by companies to offer new products/services is Amazon. Catching on the trend of healthy and wholesome food among people all over the world, heralded the launch of Amazon Fresh and Whole Foods. This is a new service offering that is likely to pay off manifold in the years to come.
Pricing is one of those core strategic choices before a company on which long term consequences are founded. How a product is priced in a market depends on how much the audience is willing to pay for it. The willingness to pay can be very effectively determined through data analytics and social listening. In fact, companies seldom enter into pricing without getting a clear picture of the audience temperament.
Benchmarking with the competitor before pricing your own product was a thing of the past. Successful organizations are already using data-backed pricing models, to great effect. In this age of hyper personalization, where products are tailored to micro segments, pricing is a dynamic offering influenced to a greater extent by demand rather than the competition.
Whether your brand needs to implement an economy pricing approach or move on to premium pricing can best be understood through data analytics.
A great example of this is the Dollar Shave Club, which implemented subscription pricing for shaving services, to great success.
What accurate prediction can achieve for the company is limited only by the company’s ambitions and the expertise of the Data analytics team. But what is the need for predictive Analysis, you may ask?
Trial and error is something that is liable to cost the company a fortune. Whether it is for a marketing campaign or the launch of a new product, it is of supreme importance to have a fair idea of how the outcome will shape up in the foreseeable future. This not only prevents incorrect investment decisions but also serves to guide the management regarding any future decisions. It is here that predictive data analysis comes into picture.
Uses of Predictive Analysis
Predictive analysis uses data, machine learning and statistical algorithms to determine future outcomes based on historical data. This is being increasingly employed by companies in functions such as risk assessment, operations improvement, fraud detection, optimizing marketing campaigns etc. It is through predictive analysis that validity is added to theoretical marketing models to approve or disprove particular marketing campaigns.
Predictive analysis is widely used in sectors such as retail, insurance, healthcare, consumer goods, e-sports etc. To learn more about predictive analysis you can Click here!
An example of the apt usage of predictive analysis is PayPal’s collaboration with Rapidminer (a predictive analytics service provider) to find out the intentions of top-tier customers and monitor their complaints.
Apart from the broad usages mentioned above, data is being used in a plethora of different ways to maximum advantage by innovative companies. A catchphrase for companies to remember being- If you aren’t data informed you are liable to repeat your mistakes. It is data and actionable analytics that companies can make faster, better-informed, and more accurate business decisions in real-time.
Honorable mentions- companies and their particular use of data:
Coca-Cola: Customer retention
Netflix: targeted adverts
Tesco Bank, Union Overseas Bank (Singapore): Risk Management
Burberry: Customer satisfaction
Spotify: service optimization
American Express: consumer behavior mapping
To read up on more such topics related to growth hacking, marketing etc. check out our GrowthFolks blogs section
These were the highlights which summarize the discussion that happened in our GrowthFolks group. The folks who have shared their views are themselves Growth Marketers/Hackers.
Thanks, Ayush Srivastava for creating the poll and Jay Karia, Akash Kamal, Pratyush Jindal, Ninad Salaskar, Sumant US, Khizer Ahmed Sheriff, Praveen Kumar, Navneet Jain, Sidharth Aima, Kritika Kamra, Sahil Patni, Piyush Bansal, Devyani Saxena, Sam Mathew, Gaurav Gururaj, and Marvin Diaz for contributing your views.
Are we missing anything important? It’s a community-driven blog, if you have anything to add, do put down your thoughts in the comment section and it will be updated here.