How Does A Digital Analytics Company Boost Business Performance?

Today, every business from finance to healthcare, retail etc. is driven by data. It plays a significant role in enhancing business profitability by enabling strategic decision making, unearthing new revenue streams and revealing hidden waste generators that eat into the bottom line. Collaborating with a digital analytics company helps companies capitalise on their benefits to improve business processes and boost their business.

To maximise the benefits of data, most data analytics companies like Neuronimbus, assemble a tech stack to form a connection between the different data sources. However, selecting a tech stack to fit in with the goals of their client can be challenging.

This selection depends on the below-given evaluation criteria or key features of the tools and platforms like:

• Goal identification: This is more important than the actual selection of the data analysis tools and platforms. Defining project goals and outlining the expectations help to formulate a well-designed strategy for data utilisation. Some aspects that businesses should concentrate on are:

o   Targeting the business issues and problems

o   Opportunities that are likely to create the highest impact

o   Customer needs

o   Real-time monitoring of business processes etc

Building the tech stack around the above core goals will enable data analytics firms to incorporate the right tools and platforms.

• Looking towards use-cases from specific industrial sectors: Surveying the tools and platforms used by other companies in the same sector helps data analytic service providers to build a successful tech stack. This survey will provide data analytic experts with a general idea about the utility of popular data analytical tools. Hence, a tech stack built based on this survey is more effective and efficient.
• Thinking about the end-user: Value maximisation of Big Data is only possible when the data strategy implemented covers all aspects of a business. Prioritising the wants and demands of the end-user will help data analytics experts to define a precise data strategy and enhance the benefits of the same.

A tech stack keeping the above factors in mind helps organisations derive the best benefit of data analysis.

Different types of popular data analytical tools

The data analytical tools that are currently available can be categorised into:

• Customer data platforms: Also known as the CDP, it works in the same way as the CRM platforms. While the CRM platform helps capture customer data to sell products or improve the sales process, the CDP is designed to obtain data from diversified sources and handle them appropriately. CDPs facilitate data gathering even from anonymous web visitors, track online and offline data and manage it properly.
• Business intelligence tools: Consisting of three main categories, BI tools or Business Intelligence tools help companies to see and make sense of their data. The three main categories include:

o   Online analytical processing that deals with ad-hoc reporting, enables data discovery, performance management, simulation models and other complicated analytical functions

o   Information delivery that provides insights into different forms of reports, visualisations and dashboards

o   BI integration helps to provide the right development environment for the data analytic strategy adopted and also enables metadata managements

While the three categories may seem different, their aim is the same. They help make organisations and companies data-driven by using processes like NLP, data mining, predictive modelling etc.

• Customer analytics tools: These tools deal with the analytical process right from its formulation to insight generation. They generally come equipped with pre-built data models that enable companies to forecast customer behaviour, understand their propensity to buy etc. The included tools also help to statistically analyse customer behaviour and optimise services, products and user experiences accordingly. Core competencies of this process include:

o   Granular segmentation

o   Statistical modelling and text analysis

o   Customer satisfaction insights

o   Acquisition, retention and churn metrics etc

• Digital experience platforms or DXPs: This new enterprise-grade software is designed to optimise user experience at every touchpoint. While DXP techniques are very similar to customer experience management or CXM platforms, the main goal of DXP is to enable strategic control over content presentation and branding.

Most search data analytics service providers build the tech stack by incorporating the above-mentioned data tools and platform. The implementation of this tech stack further helps to improve processes and customer satisfaction thereby increasing company productivity and profitability.