DATAWAVE products: Scoring
Scoring model to gauge how warm your leads are
Work with the customers who show the greatest interest in buying
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Datawave
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DATAWAVE SOLUTIONS
Effective communication with interested customers drives key metrics
We have customized the platform for your needs
Retention
User return rate
LTV
Revenue a customer generates over their entire lifetime
PU
Number of paying users
AU
Active users
Average check
Average check per user or customer
Frequency of purchases
Purchase frequency per user
DAU
Number of active users per day
MAU
Number of active users per month
WAU
Number of active users per week
GMV
Total value of goods sold on the platform
MRR
Monthly revenue from subscriptions and recurring payments
CR
Percentage of users who completed a target action
CPA
Cost of acquiring one user
ARPU
Average revenue per customer over a selected period
NPS
Customer loyalty index
IQS
Customer interaction quality score
CSAT
Customer satisfaction index
AHT
Average time spent serving one customer
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SCORING
How does it work?
We have customized the platform for your needs
We analyze the entire user journey
We build point accrual and redemption scenarios and tailor communications to the customer's interests
Initial customer contact: website forms, chatbots, widgets
We capture customer interest in the CDP and pass the data to the sales manager's CRM workspace. Integrations with:
  • Databases
  • Website and mobile app data
  • CRM system data
  • DMP providers
  • Partner network data
Examples
Real estate example

A customer is showing active interest in finished three-room apartments and is also looking at subsidized mortgage programs for large families. They visit the website often but have not yet contacted a sales manager. With Datawave Scoring and Personalization, on the customer's 3rd visit to the website in a single day, we show a banner reading “Finished three-room apartments with a practical layout for large families, ready to move in” and “Subsidized mortgage programs.” After the customer clicks the banner and lands on the page, we show a widget for contacting the call center as soon as their scroll depth reaches 60%. Once the form is submitted, the sales manager receives a notification with an assessment of where the customer is in the funnel, their interests and contact details.

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E-commerce example

A shopper who visits an online store has access to a huge selection of products. Personalization based on customer behavior analysis makes it possible to offer them exactly what they are most likely to want to buy. Real-time analysis takes into account not only their past visits to the site but even the pages they are looking at right now and the products sitting in their cart. Scoring, in turn, estimates how likely they are to buy.

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Stages:
1/
We analyze the entire user journey
Website
Social media
Sales department
Messaging channels
Mobile app
Ad networks
2/
We build point accrual and redemption scenarios and tailor communications to the customer's interests
We analyze user data:
Which products and services the user has shown interest in
What the user's interests are (based on 2nd- and 3rd-party data)
Traffic source and communication history
Timing and frequency of user interactions
Cost of communication
User actions and events across all communication channels

Which lifecycle stage the user is at within the product:

  • Searching for products or services
  • Comparing products and services with competitors
  • Deciding to buy a product or service
    Buying a product or service
  • Repeat purchase: re-entering the cycle
USER COHORT 1
90%
USER COHORT 2
70%
USER COHORT 3
83%
3/
We create communications channel by channel
We analyze user data:
Searching for products
or services
Comparing products
and services with competitors
Deciding
to buy a product or service
Buying a product or service
Repeat purchase:
re-entering the cycle
User cohorts
Interest category: product or service
Cohort 1: 90% similarity
Probability of moving to the next stage: 98%
Cohort 2: 60% similarity
Probability of moving to the next stage: 56%
Cohort 3: 88% similarity
Probability of moving to the next stage: 44%
Cohort 4: 95% similarity
Probability of moving to the next stage: 30%
Cohort 5: 40% similarity
Probability of moving to the next stage: 10%
Product and service interests
Product 1: 98%
Product 2: 70%
Product 3: 30%
Product and service interests
Product 1: 59%
Product 2: 44%
Product 3: 10%
Product and service interests
Product 1: 99%
Product 2: 60%
Product and service interests
Product 1: 98%
Product 2: 30%
Product 3: 30%
Product 4: 30%
Product and service interests
Product 1: 18%
Communication history
Email
SMS
Website
Communication history
Mobile app
Website
Communication history
Mobile app
Communication history
Website
Communication history
Mobile carrier
Telegram
Website
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4/
We build the customer journey and create personalized communications
Sending personalized messages
Website and mobile app personalization
Building trigger chains
Building trigger chains
Cohort 2: 90% similarity

Probability of moving to the next stage: 98%

WE ENRICH COHORT DATA
  • 2nd party data
  • 3rd party data
We expand the user cohort

Probability of moving to the next stage: 98%

Cohort 2: 90% similarity

Probability of moving to the next stage: 98%

WE ENRICH COHORT DATA
  • 2nd party data
  • 3rd party data
We expand the user cohort

Probability of moving to the next stage: 98%

Cohort 2: 90% similarity

Probability of moving to the next stage: 98%

WE ENRICH COHORT DATA
  • 2nd party data
  • 3rd party data
We expand the user cohort

Probability of moving to the next stage: 98%

Cohort 2: 90% similarity

Probability of moving to the next stage: 98%

WE ENRICH COHORT DATA
  • 2nd party data
  • 3rd party data
We expand the user cohort

Probability of moving to the next stage: 98%

Cohort 2: 90% similarity

Probability of moving to the next stage: 98%

WE ENRICH COHORT DATA
  • 2nd party data
  • 3rd party data
We expand the user cohort

Probability of moving to the next stage: 98%

View all
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  • increase profit from existing customers
  • improve every customer's experience
  • boost growth metrics

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