Marketing

Maximising ROI with your current marketing

budget

Optimization

Modeling

About Us Bottom-Line Analytics LLC is a consulting group

focusing on marketing optimization modeling.

Our modeling experts have a total of over 100 years of direct experience with marketing optimization modeling. This includes direct experience in over 35 countries and dozens of product categories.

We are dedicated to the principles of innovation, excellence and uncompromising customer service.

Most important, however, we are dedicated to getting tangible and positive business results for our clients.

Our experience

Sales

Macro-Economic

Factors

Distribution

Price

Mass Media: TV, Radio, Print, OOH

SeasonalityPromotion

Paid Digital Media

Other Earned Digital Media

Social Media

What is Marketing Optimization Modeling?• A mathematical technique used to indentify and quantify the

relationships between your sales and the factors that influence them

Visualize!• If you will, a system whereby you can

accurately predict the impact of your marketing plans in advance.

• A tool which will provide a precise estimate of the return-on-investment from your marketing budget and no more cat fights with finance.

• A capability where you can be confident that every marketing expenditure for each of many initiatives is deployed to generate maximum growth.

• A Pipedream? Let Bottom-line Analytics prove to you how this vision can become reality.

MMO helps to answer basic questions What are the most effective marketing channels – TV, Radio, Outdoor, Print or Digital?

What has been the ROI of paid search and banner ads?

How effective is social media?

Which marketing messages are most effective in driving sales?

How responsive are my customers to changes in price?

What is the most effective month-by-month plan for deploying my paid and earned media?

Does moving from a 30 to a 15 second commercial rotation make economic sense?

What is the best way to allocate my marketing budget?

What makes us different?1.We can help to reduce the waste in your marketing

spend by optimizing it across media channels with the highest ROI.

2.Our unique modeling approach allows us to quantify the synergistic effects of multi-channel integrated marketing. Traditional media mix methods do not allow for simultaneous and synergistic effects to be determined.

3.We have pushed the boundaries in modeling excellence and are able to measure the impact of social buzz by incorporating our proprietary SEI (Social Engagement Index)

4.We can show you how to increase sales between 4 – 8% with your current marketing budget alone.

5.Our project turnaround time is only 6 weeks (subject to data availability)

MarketingOptimizationDeliverables

Modeling

Our modeling technique proves to be highly predictive. We deliberately holdout approximately 10% of the dataset to test for accuracy.

A Highly Predictive Sales Model0

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Actual

Model

Overall predictive accuracy = 97.8%

Decomposition of sales provides a snap shot of the overall importance of media and marketing in driving total sales. In this case, 31% of total sales revenues are “due to” marketing expenditures.

68.9% 31.1%

Overall Sales Decomposition

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Online Paid Search BrandedOnline Banners EventsRadio SeasonalPrint Multi-Advertiser CoopPrint BrandedXmas Promo DM CampaignWinter Care DM CapaignSummer Care DM CampaignSpring Skin DM CampaignValentines Day CampaignMedia TV BrandedMedia TV EventsMedia TV SeasonalMedia TV Multi-Advertiser CoopMedia TV Single-Advertiser CoopMacro-Economy Baseline

Decomposition of sales across time enables us to view the incremental revenue contribution of marketing and promotional activity for specific campaigns.

Baseline sales momentum

Incremental impact of back-to-school marketing efforts

Sales Decomposition over Time

The first step in improving marketing productivity is to determine precise financial returns to marketing spending by campaign/activity.

Least efficient channels/investm

ents

Marketing Return-on-Investment

Optimal spend solution across all channelsWe conduct a mathematical optimization of your marketing spend and show you how to generate between 4-8% higher revenues without increasing total marketing investment.

Incremental Revenue 000

Current Spend 000 Optimal Spend 0000%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Xmas Promo DM Campaign

Fall DM Campaign

Summer-DM Campaign

Spring DM Campaign

Valentine's Day DM Campaign

Online.Pd.Search. Branded

Online-Banners. Events

Radio-Seasonal

Print-Branded

Print-Multi-Advertiser Coop

Media-TV-Multi-Advertiser Coop

Media-TV-Single Advertiser Coop

Media-TV-Branded

Media-TV-Events

Media-TV-Seasonal

Spend More

Spend Less

Play out marketing scenariosWe can provide an interactive dashboard that allows you to simulate different marketing mix scenarios and the resultant impact on sales.

Innovation

Unique Insights

Long Term Effects

Copy Quality Effects

Social Media Buzz

Effect

Synergistic Effects

Short-Term Effects

Message Mix

Innovations: Multi-dimensional Media Measurement

Assess marketing synergiesMarketing synergies can be assessed through simultaneous

activation of campaigns. The results of combined activation are always greater than the sum of the parts. This is a clear indication of synergies from running truly integrated campaigns.

Reve

nue

(£)

Reve

nue

(£)

Reve

nue

(£)

+31%+23%

+42%

Reve

nue

(£)

+28%

Print Media & Paid Search Synergies

Print Media & Online banners Synergies

Direct Mail & Email Synergies

Outdoor & Online Synergies

Using linguistic theory we have devised a metric that captures the behavioral patterns of social networks.

Our net positive social-media "engagement" (SEI) mirrors company seasonal patterns suggesting that the metric captures more than just social networks, with a correlation of 87%

The SEI is used as a reflection of total "word-of-mouth" and the consumer experience

Measuring Social Media

Net Positive SEI Index

1. Mine all brand related social media reviews and commentary.

2. Parse into positive & negative

review groups

3. Apply Social Engagement Index

algorithm to “score” reviews

4. Time code by week and aggregate metrics

Positive Reviews

NegativeReviews

Positive Scores

NegativeScores

Developing the Social Engagement Index (SEI)

The linkage & correlation to sales over time shows that SNI has predictive power

When incorporate this metric into a full marketing mix model we see evidence that this measurement is representative of “word-of-mouth” effects on brand performance.

ACID TEST: SEI has proven linkage with brand sales

Much like other marketing and media metrics, we can deconstruct the different elements of our SEI metric into the channels driving social engagement and brand sales.

Correlation: Facebook = 86.8%Twitter = 86.6

Source: Nielsen BuzzMetrics data as of November 27, 2011

Relative Importance of Social-Media Channels in driving consumer engagement and brand sales

Business objective: A mid-sized retail bank saw consumer loans drop 30% during the Great Recession. Now, due to efforts by the Central Bank, consumer loan rates have been dropping. Current rates of 6% are expected to drop to as low as 4% over the next two years. The bank now has decided to step-up its marketing spend and efforts in order to reverse trends in loan sales.

Solution: We undertook a comprehensive marketing-mix modelling effort which quantified the impact of media, direct marketing and digital advertising on loan demand. In addition, variables like GDP and consumer APR rates were included in the predictive model.

Result: Our modelling efforts estimated that every 1 percent reduction in consumer interest rates has about a +11% impact on loan demand. Because of the “synergy” between media and interest rates, this growth could multiply by almost a factor of 2X. One year after implementation, the bank’s loan demand increased +27 percent and the client also increased its local market share for consumer loans from 18 to 23 percent!

Case Study One: A Retail Bank

Business objective: Client has suffered 18 months of declining sales due to theglobal recession. They needed a new idea that would help re-charge sales andgrowth, across their network of 15,000 stand alone retail stores. Our task was to measure and compare returns from test markets for a new product. This product was a radical departure from their common product offerings and many in their marketing department were sceptical of its success. The test involved two markets. One market had minimal marketing and merchandising support, while the other had the national equivalent of $50 million in marketing and advertising.

Solution: The marketing mix models were developed and set up such that we could measure not only the impact of media and marketing on the new brand, but also the incremental impact or lift this product launch had on total outlet sales.

Result: Our models found a high return to the heavy spend marketing of $7.89 per dollar investment. We also found that this product launch actually stimulated a +3% increase in total store or system sales. One quarter after launching this product nationally, this client reported its first quarter of profit increase and growth in same-store sales in 18 months.

Case Study Two: Major USA based beverage & food retailer

Business objective: Client is a major hotel chain consisting of 350 properties ranging from extended-stay type of units to very high-end luxury hotels in resort areas. In 2009, this chain was just coming off of a major downturn due to a lapse in business travel and conventions from the Great Recession. They needed to follow the path outlined by Tom Davenport’s Competing with Analytics and leverage marketing-mix models in order to gain competitive advantage in their highly competitive industry.

Solution: We have conducted a series of three marketing-mix models by property and region for this client. Each engagement identified opportunities to optimize their marketing spend and generate growth from +8 to +12% by moving budge funds from less to more productive marketing activities.

Result: From the initial engagement, the clients annualized rate of growth has accelerated form -3 to +6 to +11 percent increase in revenue bookings year-over-year. The improved growth and profitability has further enabled the chain to free up capital in order to make a key acquisition that will expand their total footprint capacity by 20 percent.

Case Study Three: A Hotel Chain

Step 1 •Initial meeting. A critical meeting where we understand your current business strategies and campaigns and jointly develop project objectives

Step 2 •Data collection. We will work with local IT, Data warehouse experts and media agency to collect all relevant data.

Step 3 •Data assimilation and review with client. Review and approval of all data inputs.

Step 4 •Analytics & Modeling to provide key deliverables

Step 5 •Face to face presentation

Step 6 •Ongoing follow-ups and delivery of interactive simulator

Journey to increase ROI and Sales

mjw@bottomlineanalytics.com

404.841.1620

metricsman2010

Michael WolfePrincipalBottom-Line Analytics, LLC

678.314.8446

www.bottomlineanalytics.com

Masood AkhtarEVP EMEABottom-Line Analytics, LLC

ma@bottomlineanalytics.com

+44 7970 789 663Masood.Akhtar97

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