Marketing Technology is one of the fastest growing areas in IT. According to chiefmartec.com, the number of Mar Tech solutions over the last year has doubled. Such implementations provide the ability to automate marketing activities, allowing you to communicate with your customers faster, cheaper and in more personalized way. In connection with the capabilities of Big Data (analyzing large amounts of data from multiple sources simultaneously), companies have a chance of gaining a permanent competitive advantage in marketing.
Mar Tech with Big Data is a service for retailers, B2B, e-commerce, e-business and startups. It solves problems associated with dedicated recommendation mechanisms, the analysis of shopping habits, profiling/segmentation of customers and predicting their behavior. Such a solution is cost-effective for medium and large companies, reaching at least 300-400.000 PLN of monthly turnover – says Karol Bzik, the Manager of E-Commerce Performance Department in Divante sp. z o. o.
Divante has had E-commerce Performance Department for many years, which previously dealt with serving customers in the performance model (e-marketing, e-mail marketing, web-based analytics). Today, more and more frequently it conducts consulting projects aimed at designing and implementing IT systems supporting marketing. When working on increasing sales, there is a clear need to combine practical experience in the area of web analytics and performance marketing with the analyses of purchasing history, offer, customer profile and customer behavior.
Many companies develop their own teams of analysts and marketing and usabilityspecialists (in-house competencies). Consulting model provides maximally effective exchange of knowledge between an agency and client’s employees. In this model, both teams are complementary at every step. Additionally, consulting allows for extending and mixing competencies. Consultants who have so far focused on just one area, can also use the resources of UX, marketing and web analytics areas – emphasizes Karol Bzik.
In the recent months, E-Commerce Performance Department in Divante has created and developed a dedicated cell focused on technology and converting knowledge from data into practice in the areas of customer acquisition, purchasing retention and increasing conversion.
Divante carries out Marketing Technology and Big Data projects using Cloudera, a leading platform in this area. The support concerns the following aspects:
- Estimating sales based on data from e-commerce, mobile and retail stores, information about competition promotions and weather data (a project for a fashion chain);
- Creating dedicated recommendations to evoke secondary purchase in customers using the promotion (a project for a major retailer);
- Creating customer acquisition strategies based on the analysis of expected customer lifetime value (CLV), according to marketingsources and marketing campaigns;
- Analyzing off-line sales data for optimal selection of on-line store range (a project for one of the sales networks);
- Customer segmentation based on buying habits analysis, customer profile and shopping cart composition.
Marketing Technology and Big Data provide support for previously performed actions such as e-marketing, email marketing/marketing automation, sales offer modeling, feed management and conversion optimization and UX design.
Future trading will be based on “Actionable Data” and resulting actions, e.g. generating micro-recommendations for a client at a given time and place, modifying an offer, acquiring clients with the right profile and conducting marketing communications in accordance with customer expectations. Knowing the behavior of many users we can recommend a matching offer, segment customer profiles, facilitate subsequent steps of purchasing process, modify marketing communications or react in a situation in which the customer stops buying. Thanks to that, we can undertake more relevant actions in acquiring new customers, increasing purchasing retention and improving conversion on websites supported by the conclusions of earlier analysis of customer behavior – adds Karol Bzik.