From data assets to journey metrics

From data accumulation to journey insights, this article will delve into this evolution process and reveal how companies can move from simple data asset management to a more intelligent and comprehensive journey indicator system.

Many companies facing digital transformation are paying attention to data assets and journey indicators. After digital transformation, a common problem is data explosion. After expanding from traditional channels to online channels such as APPs and mini programs, companies will face 5 times, 10 times or even dozens of times of data growth. In addition to bringing challenges to technical architecture, it will also bring about a simultaneous increase in data assets and the pressure of verifying their value.

The pressure of business application growth and the pressure of data asset growth are synchronized, and will ultimately come back to a soul-searching question: What is the value of data asset growth?

The solution to the dilemma of data asset accumulation mainly revolves around two parts: high-quality growth of data assets and continuous accumulation of journey indicators.

1. High-quality growth of data

Under the trend of digital transformation, the core of data growth is the rapid growth of customer data. From customer behavior and status to product data, all will show a surge.

After abstracting customer data, you can think of it as an Excel file. The rows and columns in a table form a database. The growth of the "rows" in the table is mainly due to the growth of users and journeys; the growth of the "columns" in the table is mainly due to the growth of customer and product complexity, such as the growth of user tag attributes; and the growth of the "retention" in the table depends on continuous positive feedback. Taking car companies as an example, the growth of car sales is the growth of "rows", while the expansion of car models, the increase of product lines, and the increase of shopping options will make customer needs more complicated, thus bringing about the growth of "columns".

How to maintain high-quality growth of the "line"? We need to redefine "users", sort out the "journey", and liberate the idea of ​​data growth. Generally speaking, we can think of "customers" as people who have made payments or consumption behaviors, and "users" expand the concept of "customers". People who pay attention to the brand and understand the product before paying are our users. For traditional transforming companies, the changes brought about by this difference prompt us to rethink journey design, from customer-first to user-first. The application of digital advertising, landing page factories, and intelligent customer service technology all belong to this part. Shifting the perspective from online to offline, the digital upgrade of the production tools of front-line service employees has quantified more of the original offline customer journeys, especially the nodes that affect customer service.

How to maintain the rapid growth of "columns"? Column growth reflects the growth of different attribute data such as user needs, consumption potential, journey stages, etc. Appropriate product capabilities can help us better obtain column expansion. Search functions and product details pages can help us obtain user demand intentions and degrees; evaluation feedback can help us obtain customer attitudes and experiences; mining and refining through forms or algorithms can help us obtain user lifestyles and potential. Obtaining more user behavior data can help us do better in user insights.

After the "rows" and "columns", continuous positive feedback needs to be formed. Accurate and fast data is the core of positive feedback for high-quality and continuous growth of data. The inaccurate data collection is partly due to technical reasons, but more due to the alignment of business, data, technical cognition and semantics. The "fast" data is essentially how to meet the demand for data under limited computing power. Therefore, it is necessary to have a clear definition of the application scenarios of data. The key is to distinguish between offline, quasi-real-time or real-time computing under different users and business scenarios.

2. Growth of Journey Metrics

There are three journey indicators: based on the pre-assumed business knowledge, the data aggregation calculation rules are designed to obtain the customer journey description system. Journey indicators are designed to solve business problems in a quantifiable and verifiable way, and to continuously optimize business performance based on this.

The process of combing journey indicators is divided into four steps:

1. Definition of goals and customer groups: clarify business goals and corresponding user groups;
2. User demand stage definition: Through customer surveys or expert interviews, clarify and define the development path and stages of the needs of this type of users. There are some ready-made models that can be used as reference, such as AIPL for retail and AARRR for the Internet;
3. Sorting out user explicit behaviors: Sorting out typical user behavior result types based on the user's subjective thoughts and touchpoint interactions at each journey stage;
4. Journey indicator mapping: Based on the performance of the above data indicators, we can sort out the behavioral result sets that reflect each stage of the customer demand journey. For example, for AIPL’s brand “understanding”, we can see which specific touchpoint behaviors are involved, how many times they occurred, how long they stayed, etc. It can be considered that the user has completed the “understanding” of the brand.

3. Customer Journey Analysis

After you have sorted out your journey metrics, you need to analyze them. Customer journey analysis is divided into three steps:

1. Check the current situation: After sorting out the journey indicators, review the overall data of the journey customer coverage, data richness of each node, turnover ratio, etc., to determine whether the overall journey design is reasonable;
2. Find opportunities: Observe the conversion rates between nodes, identify typical “low” and “slow” blockages in the journey, and adjust the journey design to maximize value;
3. Judging potential: Business iteration has costs. Some journey nodes are "low" or "slow" due to business model limitations or industry ceiling limitations. You need to refer to peer data or look at the data fluctuations of the node over a longer time dimension to judge its business improvement potential.