1. Analysis background introduction
This case comes from a Jingdong beauty
brand flagship store, mainly engaged in medium and high-end beauty products,
and is committed to providing consumers with high-quality makeup solutions.
Relying on the JD platform, the store uses big data and intelligent
recommendation system to continuously optimize the product structure and
customer experience, attracting and maintaining a large number of loyal
customers.
In the pastIn the past few years, the store
has accumulated considerable traffic and market share through accurate market
positioning, excellent after-sales service, continuous brand promotion and
other means. With the increasingly fierce competition in e-commerce, how to
effectively improve the traffic conversion rate, reduce traffic loss, and
maintain brand growth has become the key to the current store development.
2. Statement of key issues
The traffic conversion rate is low. How to
improve the purchase conversion rate of visiting users?
At present, the traffic of stores is
growing steadily, but the conversion rate is relatively low, which means that a
large number of potential customers have not finally completed the purchase.
Analyzing the factors affecting traffic conversion and exploring how to improve
the purchase intention and actual conversion of visiting users by optimizing
page layout, product display, price strategy, promotional activities and other
means are the core problems facing the store at present.
3. Analyze the plan
3.1 Select key data indicators.
|
Serial
number
|
Name
of the indicator
|
Paraphrase
|
Analysis
angle
|
|
1
|
Number
of people to search
|
The
number of searches refers to the number of independent users who search for a
store, product or keyword through a search engine or platform within a
certain period of time.
|
This
indicator reflects how many people take the initiative to come into contact
with the store or a certain product. The number of searches is usually
closely related to users' interests, needs and brand awareness.
|
|
2
|
Number
of views
|
The
number of visitors refers to visiting and viewing stores within a certain
period of time orThe number of independent users of a specific product page.
|
Unlike
the number of searches, the number of views reflects whether the user hasI
became interested in the product and further checked the product details.
|
|
3
|
Year-on-year
growth rate
|
The
year-on-year growth rate refers to the comparison of the growth rate of an
indicator (such as the number of searches, the number of views, the number of
purchases, etc.) in the current period of time and the same period of the
previous year.
|
It
is usually expressed in the form of a percentage, reflecting the trend of the
indicator in the same time period.
|
|
4
|
Year-on-year
increment
|
Year-on-year
increment refers to the actual increment of an indicator in the current
period compared with the same period of last year, which is usually a
specific numerical change.
|
For
example, the number of searches in a month was 100,000, compared with 80,000
in the same month last year, with a year-on-year increase of 20,000.
|
|
5
|
Browse
the purchase conversion rate
|
The
browsing purchase conversion rate refers to the proportion of users who have
browsed the store or product page within a certain period of time to make a
purchase. The formula is: browsing purchase conversion rate = number of
buyers/number of browsing
|
This
indicator reflects the effect of the store or product page in attracting and
persuading consumers to buy.
|
|
6
|
Year-on-year
purchase conversion rate
|
The
year-on-year purchase conversion rate refers to the comparison between the
purchase conversion rate of the current cycle and the purchase conversion
rate of the same period of the previous year, which is usually expressed as a
percentage.
|
This
indicator reflects the year-on-year trend of purchase conversion rate.
|
Description: These indicators together
constitute a complete conversion process analysis framework from user search,
browsing to final purchase. Through the careful analysis of each indicator, the
quality of store traffic, consumers' purchase intentions and the effect of
various marketing activities can be effectively evaluated.
3.2 Power BI Visualization Scheme
Note: The DEMO page data is simulated data,
which is for reference only to the analysis angle and Power BI function
display, and does not involve any actual business data.
4. Analysis and interpretation
The number of searches and views can help
brands identify traffic sources, traffic quality and user interests.
The year-on-year growth rate and
year-on-year increment provide a comparative analysis perspective, which is
helpful to evaluate the growth trend and actual effect of the brand in the
market.
Browsing the purchase conversion rate and
purchase conversion rate year-on-year helps to measure the impact of pages,
marketing strategies, product positioning, etc. on consumers' purchasing
decisions.
Refine the search, browsing, purchase and
other indicatorsAfter coming to the dimensions of product line, product
category, store, member and non-member, we can more accurately understand
consumer behavior and improve the effect of products and marketing strategies.
Refined analysis not only helps to discover market opportunities, but also
helps brands make more refined management in supply chain management, inventory
optimization, member operation and other aspects.
Compared with the flow trend and the
conversion trend, time trend changes can help predict future demand changes,
especially during special periods such as seasonal fluctuations, promotional
activities, holidays, etc. Understanding these trends is crucial for accurate
resource allocation.
5. Application effect
1. Analysis by product line
Refine the flow conversion analysis to the
product line level,It can help enterprises more clearly understand the
performance differences of different product lines and provide data support for
product management and inventory decision-making.
Business guidance effect:
Optimize the product line structure: By
analyzing the number of searches, number of views, purchase conversion rate and
other indicators of each product line, we can find out which product lines
perform well and which may be slugging. For product lines with poor
performance, you can consider optimizing or adjusting the product (for example,
replacement, re-pricing, repackaging, etc.).
Resource allocation optimization: For
product lines with excellent performance, resource investment can be increased,
including advertising, inventory allocation, supply chain security, etc., to
ensure that it can be met in time when demand grows.
CustomizationPromotion strategy: There may
be differences in customer groups of different product lines, and detailed
analysis can help enterprises customize more effective promotions for each
product line. For example, for product lines with high conversion rate,
exposure can be increased; for product lines with low conversion rate,
discounts or coupons can be provided to stimulate purchases.
2. Analysis by product category
The refinement of the analysis to the
product category level can help understand consumers' preferences and
purchasing behaviors in different categories of products, so as to further
optimize the product portfolio and classification.
Business guidance effect:
Improve the accuracy of product
recommendation: Through the analysis of the purchase conversion rate of
different product categories, it can identify which categories of products need
to be further optimized.For example, if a category has high views but a low
purchase conversion rate, it may be necessary to optimize the page content,
price strategy or inventory level of that category.
Targeted promotion: After being refined
into product categories, marketing promotion strategies can be customized
according to the sales performance of different categories. For categories with
high purchase conversion rate, more promotion can be carried out through social
media or precision advertising, while for categories with low conversion rate,
users can be stimulated to buy through coupons, limited-time discounts and
other means.
Adjust the product portfolio: Through the
analysis of the year-on-year growth rate and year-on-year increment of each
category, we can understand the market demand trend of each category. For
example, if the year-on-year growth rate of a category is low, it may be due to
reduced market demand or fierce competition. At this time, you can consider
adjusting the category of goods or stopping the production of some products
that are no longer popular.
3. Analysis by store
Detailed analysis of traffic and conversion
rate by store, especially if the brand has multiple stores or distribution
channels, it can help evaluate the sales performance and potential problems of
each store.
Business guidance effect:
Store operation optimization: Through the
analysis of the number of visitors and the purchase conversion rate of
different stores, we can find out the stores with poor performance and help
identify potential operational problems. It may be that the store page design
is not attractive, the product display is insufficient, or the store
advertising promotion is insufficient.
Cross-store resource allocation: If the
brand has multiple stores, resources can be flexibly allocated according to the
sales situation of each store. For example, a certain storeThe store traffic is
high but the conversion rate is low. The user experience and conversion rate
can be improved by optimizing pages and improving customer service. On the
contrary, the conversion rate of a store is relatively high, and more needs may
be met by increasing inventory.
Regional market differentiation: If
multiple stores cover different geographical areas, the analysis of traffic and
conversion rate can reveal regional differences. For example, consumers in some
regions may prefer certain specific products or services, and enterprises can
adjust the display, promotion or pricing strategies of stores in different
regions accordingly.
4. Analysis by members and non-members
Refine the analysis of member and
non-member groups, which can deeply understand the differences in the
purchasing behavior of different types of customers, help customize marketing
strategies, improve the member conversion rate and optimize member management.
Business guidance effect:
Member loyalty management: analyzing the
purchase conversion rate of members and non-members, browsing the purchase
conversion rate and other indicators can help the brand identify the loyalty
and activity of members. If the purchase conversion rate of members is higher
than that of non-members, it means that members are more loyal to the brand.
Members' stickiness can be further enhanced through personalized
recommendations, regular promotions, points rewards and other means.
Member conversion strategy: For non-member
groups, analyzing their number of visitors and purchase conversion rate can
assess the conversion potential. For these users, they can be guided to
register as members by providing first-time purchase discounts, free trials,
and membership rewards.
Customized preferential strategies:
According to the analysis of the purchase behavior of members and non-members,
the preferential strategies of different user groups can be adjusted.For
example, inductive preferential activities (such as registration discount) can
be launched for non-members, while exclusive discounts or points rewards can be
launched for members to enhance the purchase frequency and stickiness of users.
Accurate marketing: Member data often
contains users' detailed purchase history and preferences. Through the
comparative analysis of the behavior of members and non-members, marketing
activities can be formulated more accurately. For example, promotions for
non-member users may focus on new product trials and first-time shopping
discounts, while for member users, they focus on deeply customized offers and
priority purchase privileges.