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Predictive Analytics for Business and Marketing

INTRODUCTION

If you want to predict how customers will respond in the future, you need to turn to predictive analytics. By learning from your abundant historical data, predictive analytics provides something beyond standard business reports and sales forecasts: actionable predictions for each customer. These predictions encompass all channels, both online and off, foreseeing which customers will buy, click, respond, convert or cancel.

The customer predictions generated by predictive analytics deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit. For online applications such as e-marketing and customer care recommendations, predictive analytics acts in real-time, dynamically selecting the ad, web content or cross-sell product each visitor is most likely to click on or respond to, according to that visitor's profile.

DURATION

3 days

WHO SHOULD ATTEND?

Managers. Project leaders, directors, CXOs, vice presidents, investors and decision makers of any kind involved with analytics, direct marketing or online marketing activities.

Marketers. Personnel running or supporting direct marketing, response modeling, or online marketing who wish to improve response rates and increase campaign ROI for retention, upsell and cross-sell.

Technology experts. Analysts, data scientists, BI directors, developers, DBAs, data warehousers, web analysts, and consultants who wish to extend their expertise to predictive analytics.

TOPICS TO BE COVERED

Solving business problems with predictive analytics

Predictive analytics solves many business problems, offering solutions such as:

    • Increased customer retention by predicting defection

    • Increased online conversions and ad takes by predicting clicks

    • Increased sales and acquisition rates by predicting cross-sell opportunities

    • Personalized web and email content by predicting online response

    • Greater relevancy by predicting customer needs

    • Increased direct marketing response with response modeling

    • Decreased campaign spending by predicting non-responders

    • Increased fundraising profit by predicting donations

    • Higher-valued acquisitions by predicting customer lifetime value

 Creating predictive models

Data is your most valuable asset. It represents the entire history of your organization and its interactions with customers. Predictive analytics taps this rich vein of experience, mining it to produce predictive models. Where multi-channel data is available, predictive analytics discovers interactions across customer touch points, such as key online behavior that may predict which customers will respond to direct mail.

Whatever the application, the core methodology of predictive modeling is the same. We will uncover, in concrete terms, how modeling transforms your data into actionable customer predictions. To this end, we will see exactly what a model is, taking a look inside to see how it works and how it is created. Then we will:

    • Explore several example models in action

    • Turn the knobs that tweak and control modeling

    • Compare and contrast modeling methods intuitively, visualizing their differences so it all makes sense:

      • Decision trees

      • Business rules

      • Naive Bayes

      • Linear regression

      • Logistic regression

      • Neural networks

      • Other more recent advanced modeling techniques

Live demo of predictive analytics software.

Measuring how well predictive models work

Once you've got a predictive model, how do you know how good it is? We cover methods to evaluate models, which fall into two groups:

Forecasting: How large a boost in revenue, sales or profit will the model produce?

Accuracy: How well does it predict, how often is it correct, and how much better is it than standard segmentation such as RFM?

 Management and project leadership for predictive analytics

Although predictive analytics is technical at its core, it must be run as a business activity in order to generate customer predictions that have a business impact. This requires a wholly collaborative process driven by business needs and marketing expertise. This ensures that customer predictions are actionable within your company's operational framework, and that they have the greatest impact within your company's business model.

We explore this process, by which analysts and managers collaborate to strategically position predictive analytics, sustain universal buy-in and understanding, and avoid common roadblocks and unforeseen hazards.

REQUIREMENTS

Participants should be reasonably proficient in English. Applicants must live up to Indepth Research Services (IRES) admission criteria.

METHODOLOGY

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

ACCREDITATION 

Upon successful completion of this training, participants will be issued with an Indepth Research Services (IRES) certificate.

TRAINING VENUE

The training is residential and will be held at IRES training Centre. The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

ACCOMMODATION

Accommodation is arranged upon request. For reservations contact the Training Officer.

Email: This email address is being protected from spambots. You need JavaScript enabled to view it..  

Mob: +254 715 077 817

Tel: 020 211 3814

TAILOR- MADE

This training can also be customized for your institution upon request to a minimum of 4 participants. You can have it delivered in our training centre or at a convenient location.

For further inquiries, please contact us on Tel: +254 715 077 817, +254 (020) 211 3814 or mail This email address is being protected from spambots. You need JavaScript enabled to view it.

PAYMENT

Payment should be transferred to IRES account through bank on or before C.O.B. 21st  October 2019

Send proof of payment to This email address is being protected from spambots. You need JavaScript enabled to view it.

CANCELLATION POLICY

Payment for the all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

1.     Participants may cancel attendance 14 days or more prior to the training commencement date.

2.     No refunds will be made 14 days or less to the training commencement date. However, participants who are unable to attend may opt to attend a similar training at a later date, or send a substitute participant provided the participation criteria have been met.

Please Note: The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

Event Properties

Event Duration 3 Days
Event Date 28-10-2019
Event End Date 30-10-2019
Cut off date 21-10-2019
Individual Price(Kenyan) KES 46,999
Individual Price (International) EUR 539
Individual Price(International in Dollars) USD 627
Location Nairobi, Kenya
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