Eng
Eng
The modern consumer has become more demanding and informed, and less loyal at the same time. Consumer behavior undergoes constant transformation, and it is already impossible to predict through traditional approaches. When interacting with businesses, the consumer leaves a digital trace everyday which is an unprocessed gold mine. The size of data left over the course of the interaction may reach tremendous scales. In order to gain and keep consumers on board in this competitive environment, the skill of collecting, processing and analyzing the said mega data has become a strategic advantage. Thus, those who are customer centric prevail.
Customer-centric analytics help businesses have a better understanding of customers’ interests and allow them to observe consumer behavior over time. It also enables companies to predict what kind of configuration of a product or service will be most useful for consumers in the future.
Establishment of analytical capacities in a company enables:
|
|
|
|
|
|
|
|
|
|
|
|
It is logical that consumers positively assess the establishment of consumer-centric analytics in the company that is later reflected in consumer satisfaction and loyalty. The following factors boost satisfaction:
|
|
|
|
|
|
A permanent and stable increase of client base is a goal every single business strives for, but only very successful companies achieve this. The secret behind those successful companies is that they can predict and model consumer behavior better than others. Customer-centric analytics combined with data visualization techniques is a tool that helps a company understand who their consumers are, and what they want and plan for in the future.
A consumer study starts with a behavioral and purchasing segmentation. Segmentation is diverse and can be based on historical data accumulated in the company as well as deep insights collected through consumer market studies. In addition to complex segmentation approaches, it is often very useful to conduct consumer analysis based on characteristics such as demographic profiles, geographic data, sales channels, time of interaction with the consumer, etc. One such primary technique is RFM segmentation which is widely used because of how simple it is to interpret and implement.
Consumer basket analysis takes a special place in business analytics as it acts as an important precondition for conducting effective targeted marketing.
Both CLA – Customer Lifecycle Analysis and CLV – Customer Lifetime Value analysis can bring significant benefits for a business. By combining these two methods, we can identify which segment is inclined to loss or discover the most valuable consumers at any time.
The prediction of consumer behavior is the foundation for consumer preservation and growth strategies. Prediction analytics include:
Nowadays, when competitive offers are so diverse – boosting your sales means being more precise, more creative and more fast-acting than your competitors. The winner is the one that concentrates on the most profitable segment, knows exactly what and when to offer, and to whom.
To support the cross-sell and up-sell sales strategy, it is of particular importance to develop a recommendation model based on which individual offers tailored to a specific consumer is generated in a real or passive mode.
The recommendation engine is an integral part of targeted marketing and is directly responsible for generating the Next Best Offer to consumers.
Through establishing consumer-centric analytics, sales can increase by 10% and more in targeted segments. Being oriented on profitable consumers or product improves a company’s financial performance.
Effectiveness has turned into a main principle of the modern corporate world. Nowadays, businesses are well-aware that under global competition, earning a dollar more or gaining one more client or keeping them on board costs much more expensive. In turn, targeted budget spending, finding the shortest and cheapest way to communicate with customers, consistent monitoring of the results and increase in ROI – these have become the main functions of marketing.
Consumer segmentation and value analysis help us in the effective allocation of a marketing budget enabling the introduction of a budget limit for each segment. This kind of analysis can be conducted through a prediction CLV model which shows total profit (or income) to be generated by the consumer in the future in current prices. The given approach is often utilized to financially estimate the value of a consumer’s briefcase.
Our Approaches
Establishment of customer centric analytics is a complex work which requires tight cooperation with a client and consideration of industry’s specifications. ACT’s business analytics team is comprised of experienced specialists: data sciences and engineers, analysts and visualization specialists. We apply Data Science, Machine Learning and AI instruments to offer the best technological and architectural solutions to our clients. 18 years of experience in data analysis and statistics certainly comes in hand to do the job comprehensively.
We speak the language of business which is particularly important in terms of business analytics. Our business consultants and project management team is a valuable asset that is capable of translating business tasks in technical language, plan their enforcement and turn project-based, short-term cooperation into long-term mutually beneficial partnership.
In addition to data processing and modelling, ACT establishes business analytics tools (BI Tools). The given tools provide data visualization in interactive and simplified form that makes it easier to make business decisions and provides comprehensive picture of the processes taking place in business.
Establishment of business analytics implies transformation of technologies, technical infrastructure, processes, human resources and workplace in the company. ACT team helps clients utilize results collected through consumer analysis as effectively as possible and ensure fully successful digital transformation.
