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SAP INNOVATION FORUM ISTANBUL

SAP Predictive Analytics for Sales and Marketing

Aykut Tellibayraktar

DIGITAL ERA

Connected Innovation

© 2014 SAP SE or an SAP affiliate company. All rights reserved. 2Customer© 2015 SAP SE or an SAP affiliate company. All rights reserved.

Our Target…

Veri

Madenciligi


Sales & Marketing Use Cases

Prospect new customers

Grow existing customers

Identify high value customers

Retain potential churners

Customer segmentation

Social network analysis

Geographical analysis

Demand Forecast


Prospect Identify new customers

Target prospects with profiles similar to your existing customers.

Reduce costs of acquisition: do not spend time with people you will never convince

Increase conversion rates: target only the most promising prospects

1 - Identify typical customer profiles 2 – Target promising prospects

Current customer profiles

Predictive model Identify best targets

SAP Predictive models can be integrated into marketing campaigns, CRM and other applications to

automate the targeting process and provide predictive information to all business users.


GrowCross-sell and Up-sell existing customers

Increase returns from cross and up-sell campaigns by identifying Next

Best Action based on customer profile and previous purchases.

Use predictive models to:

- Evaluate appetency to purchase new products and services using

customer profile (age, gender, profession, region, typical basket…).

- Identify common product associations and next best offer for each

customer.

- Select the best channel to contact customers and capture their interest.

SAP predictive recommendation models can be integrated in real time with

applications such as merchant web sites and mobile applications for sales

forces.


SampleProspect & Grow

49.000 total customers

12.000 bought product X

1. Identify the customer profiles who prefer my product.

2. Who in my current customer pool can also be interested in this product X?


Customer valueEarly detection of high-value customers

Identify low or medium value customers that will become your premium customers in the

future and make them loyal!

Identify current key customers and analyze their past lifecycle

Detect new customers following the same path

1 2 3 4 5 6 7 8 9 10 11

Different customer values across time

Customer 1 Customer 2 Customer 3


Retain customersIdentify potential churners

Customer acquisition costs are much higher than customer retention costs.

Digitalization and competition make customers more likely to churn.

SAP Predictive Analytics helps companies to:

Identify customers at risk of leaving

Select offers and incentives most likely to

persuade each customer to stay loyal

Use social network analysis to predict viral churn


Customer segmentationGrouping customers on their similarities

Segmentation helps you to divide your customer base into groups

of individuals that share similar characteristics

Identify high value and/or high risk profiles

Target specific groups of customers effectively and allocate marketing

resources to best effect

Invest resources to tailor products and services to match the needs of each

target segment

SAP Predictive Analytics analyzes your customer data and

identifies the most relevant customer attributes. Customer

segment are optimized through

Goal-directed segmentation

Automated attribute selection

Graphical segment analysis

Customer base

Homogeneous groups

Personalized strategy


Social Network AnalysisAnalyze the shape of your social network

SAP Predictive Analytics provides a unique spectrum

of social network analysis capabilities so that you can

understand social influence and behaviors across

customer communities.

Social data comes from various sources, such as:

Social networks from the web

Call detail records in the Telcos

Financial networks to detect fraud

You can then integrate social attributes into all of your

predictive modeling to optimize campaigns across the

customer lifecycle whether it’s member-get-member, cross-

sell, up-sell or retention.


SampleProduct Recommendation

8400 Transactions

795 customers, 1263 Products, 17 Product Groups

1. Which Products or Product Groups are sold together?

2. Which products may I offer for a specific customer?


Geo-locationLocation aware marketing

We personalize customer interactions based on

an individual's location.

Location aware: translate latitude and longitude data

into a new geographic variable called “tiles” or regions

Co-Location: detects transactions or events occurring

in the same location during the same period of time

Path Identification: creates a sequence of geo-

localized positions from time-based transactions to

extract patterns of events

Natively integrates geo-localized data and derived

location-based attributes into predictive modeling


Demand forecastOptimize stocks and resources

Time-series models capture multiple effects to explain and forecast demand for goods

and services:

Trend

Seasonal pattern

External effects

• Weather / Temperature

• Price

• Advertisement…

Demand forecast helps companies managing their stocks and avoid shortages.

It is also widely used to optimize human resources management in stores and call centers.

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