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The sudden hike in demand for data scientists all over the globe is a strong cue for the fact that data is going big and increasingly popular. Businesses are leveraging the power of data to gain positive tractions for their organizations. We’ve chalked out a number of pointers that highlight the ways in which data analytics can bring in revenue and visibility to your organization brand.
Customer behaviour Analysis
Customer behaviour analytics is primarily about understanding how customers act across each sales channel and interaction point either digital or non-digital – and what impacts their actions & outcomes. It gives you a way to implement – address the right audience group, through the right channel, with a right message, at the appropriate time.
Past and present customer buying behaviour can be analysed to predict customer behaviour patterns. Which products do customers like? What works and what doesn’t? Based on this analysis, data can be assimilated into your business strategy. A company can then utilize this data to stock up on products that customers are purchasing.
Target the right group
Data can be utilized to identify your organization’s core target customer. Data that is collected from online information about customers can be stored in a data lake for performing data analytics. The processed data then becomes vital for targeting marketing strategies geared towards a particular group of / individual audience. The audience can be of a certain age group, nationality, or gender.
Reinvent your marketing strategies
Go one step ahead with your advertising plan by charting out the right communication approach. Data can greatly help you with this. For example, your brand tone can be changed to match the preferences of your target audience.
Data analytics can be used to not just set down communication strategies and analyse consumer behaviour. You can also use data analytics to identify the strengths and weaknesses of your employees. Data collected from their work inventory can be stored in a data lake and with analytics performed on this data it becomes easier for an employer to identify if things are going wrong. Such refined & processed data/ information, could help on organizations in employee performance appraisal process, deciding on necessary interventions, training sessions. This goes a long way towards strengthening employer gratification and improvement in the skills of employees.
The fundamental tenet of supply chain management is to control the manufacture, storage, transportation and sale of goods-services to meet customer demands. Data analytics can be leveraged to optimize these four critical aspects of supply chain. Basically, to identify what sells and what doesn’t in what qualities. Is a certain product doing better than the others? If yes, you can then stock up your inventory with the product which is in demand. This will help a company conserve resources and on possibly redundant expenditure. Extra costs are decreased and what sells is brought in bulk. It can prove to be a win-win strategy plan.
The analysis strategies discussed till now can be extended to marketing areas as well. Advanced analytics is going to define the future of digital and non-digital advertising. Per one of the marketing research reports, advertisers are planning to increase the average number of integrated data sources from 5.4 today to 6.2 before 2020 to gain greater insights & advertising effectiveness. Data analytics is able to capture which products are bought together by customers. For example, a pattern might emerge of customers buying sneakers with hooded jackets. Using this data, companies can then implement cross promotion offers. This leads to improved top line as well as bottom line for the organization.
Enabling Data-driven insights
In today’s rapidly changing and growing data environments, data-driven insights can be used as a disruptive force to take your organization ahead of competition. . However, data growth is one of the most critical challenges faced by organizations today. With an effective data management strategy, organizations would be able to address the impact of data complexity, enabling them to pave the way to a data-driven analytical culture and make business decisions based on such insights.
Category: Data Analytics, RPA & AI