With the evolution of online marketing and the adoption of digital video recorders, advertisers are re-examining the allocation of marketing spending.
An effective mix of traditional and online advertising increases volume,
drives brand equity and reduces price sensitivity.
Optimize traditional vs. online advertising spending
Reallocate marketing funds based on efficiency levels
Understand the impact of campaigns, day-parts & weights on profitability
Maximize advertising efficiency through benchmarking
Develop synergistic advertising, trade & consumer promotion strategies
Online Vs. Traditional Measurement
SMS marketing mix models mathematically decompose the impact of online, traditional advertising and non-advertising variables on volume:
Television Advertising Variables:
- Short & Long-Term Volume Impact of Ad Campaign
- Copy & Weight Testing
- Day-part Evaluation
- Network vs. Local TV
- Continuous vs. Pulse Advertising
- Halo & Synergistic Effects
Online Advertising Variables:
- Social Media
- Search Engine
Other non-TV/Online advertising Variables:
- Radio, Outdoor, Facings, Sampling, Consumer, Trade, Price, Print, PR
Online advertising is targeted and effective in increasing awareness and volume.
As marketers migrate to the digital space, online advertising costs are increasing and likely to result in lower return on ad investment.
Allocate optimum spending between social media, search, banner & email
Maximize online efficiency through benchmarking
Understand the relationship between online-driven awareness & volume lift
Quantify the impact of cross media on volume & profitability
Leverage online advertising to reduce price elasticity among key demographics
Online Vs. Traditional Measurement
SMS point-of-sale and survey-based marketing mix models separate the impact of online vs. traditional media on volume. This is achieved by measuring awareness levels associated with various forms of media and by integrating these factors in our volumetric predictive models.
SMS Online Measurement Analytics
Our integrated modeling, based on survey and point-of-sale data, delivers four key strategic insights to advertisers:
Advertising volume decomposition & ROI
Viewership measurement & tracking
Share of online voice impact on volume
Online awareness impact on stated intent to purchase
SMS is a recognized leader in the area of strategic pricing.
Our solutions maximize volume and profitability through the application of statistical models to point-of-sale, shipment, household and survey data.
Quantify product price elasticity by consumer segment & retailer
Optimize trade promotion spending
Implement pricing & promotional practices that reduce price sensitivity
Identify key absolute & relative thresholds
Develop & execute an effective competitive pricing strategy
We work collaboratively with clients to develop, execute and implement successful everyday and promotional pricing tactics that maximize sales without eroding brand equity.
Our pricing strategists understand the U.S. consumer market as well as current retail and economic trends, enabling them to present analytic results to clients in a commercially relevant context.
Pricing Analytics: Demand Modeling
SMS proprietary and extensively validated predictive demand models are based on the Bayesian Hierarchical architecture, providing clients with the following insights:
Shelf or non-promoted price elasticity
Competitive price cross elasticity
Price threshold testing
Promotional price response
In-store deal lifts
Category price response
Economic impact on category and key brands
Pricing Analytics: Controlled Store Testing
& Survey Modeling
In rapidly changing economic and inflationary environments, point-of-sale demand models alone may not provide a forward-looking view of emerging consumer behavior. SMS offers the following services to help clients develop an effective pricing structure in dynamic consumer environments.
SMS Controlled Store Testing (CST) to measure the impact of price points significantly outside current retail price ranges.
Survey or panel based Discrete Choice Modeling (DCM) to identify emerging and forward-looking consumer attitudes about price and value.
The right mix of products on the shelf or in a multi-brand portfolio is critical to the performance of a brand and the category.
Marketers make assortment decisions based on sales-to-space ratio analyses, consumer preference and product attribute modeling.
Maximize brand, SKU & category incremental volume
Eliminate unprofitable product redundancies
Rationalize products at retail, based on feature preference
Develop assortment-driven trade promotion programs
SMS Dynamic Competitive Modeling™
SMS proprietary Dynamic Competitive Modeling™ system is the leading store-level assortment and category optimization solution. Built on the transferable demand architecture, it provides a detailed map of factors that drive product purchasing and switching behavior.
How It Works
STEP-1: Purchase structure is mapped based on volumetric interactions of all major SKUs in the category.
STEP-2: Drivers of switching behavior (product attributes, distribution, pricing, promotions and advertising) are identified.
STEP-3: Economic and environmental factors are identified and removed from predictive models.
STEP-4: Dynamic Competitive Models™ produce a full range of simulations based on various assortments and changes in marketing stimuli.
Testing is a cost effective and accurate measuring mechanism
Through testing, marketers can reduce product launch risk and identify in-store merchandising programs that will deliver maximum volume and profitability.
Quantify & mitigate risk
Fine-tune product launch & repositioning
Test consumer response to new price points
Assess customer acceptance of new products, line extensions & new packaging minimizing risk of failure before launch
SMS Testing Approach
SMS testing services are based on experimental design architecture, employing multi cell store or panel structures to read test treatments.
ANCOVA/ANOVA TEST DESIGNS
Quantify the impact of post promotion in-store programs on volume and profitability. Following the application of data transformation algorithms on client-supplied test & control cell data, SMS conducts ANCOVA-based modeling to measure the net change in sales attributable to test variable.
CONTROLLED STORE TESTING (CST)
This approach is the primary measurement vehicle for analyzing the effect of in-store marketing and new product features on volume, prior to national launch. Proprietary data transformation and ANCOVA modeling deliver accurate and representative insights to both manufacturer and retailers. SMS manages the complete testing process from in-store execution to modeling.
PANEL & SURVEY OVERLAYS
Further measure the impact of marketing activity on awareness and purchasing. Panel and survey overlays align penetration, buying rates and consumer purchase behavior with point-of-sale volume. Results are integrated with point-of-sale test data to provide a full understanding of test variable impact.
Survey data and analytics provide an understanding of the ‘why’ behind the ‘what’.
Historically, the challenge with primary research has been that panelist stated behavior has not necessarily translated into actual purchasing.
Understand the impact of awareness levels & product usage on actual volume
Align marketing spending with consumer needs, attitudes & behaviors
Leverage consumer preference to fine tune point-of-sale marketing
Develop consumer segment strategies based on growth potential
Simulate how consumers evaluate offerings vis-à-vis other alternatives in the market place
Design new products/line extensions or reposition existing products focusing on the most important attributes
SMS integrates primary research data and survey-class models with point-of-sale and economic data to provide a complete understanding of emerging and forward-looking purchase behavior.
SMS Approach to Survey Analytics:
STEP-1: Design survey and collect data based on client objectives and expected downstream modeling requirements.
STEP-2: Conduct traditional battery of standard descriptive analyses
STEP-3: Apply conjoint and discrete choice models to build a predictive map of factors that drive consumer choice (product attributes, price, advertising, internet, etc).
STEP-4: Integrate survey analytics with transaction-based demand models (price modeling, promotion modeling, marketing mix modeling) to gain a complete understanding of factors that drive consumer awareness, preference and purchase behavior.