WHITEPAPER POINTLOGIC VALUEPOINT ADVANCED MEDIA ANALYTICS AND CHANNEL PLANNING SOFTWARE

Transcription

1 WHITEPAPER POINTLOGIC VALUEPOINT ADVANCED MEDIA ANALYTICS AND CHANNEL PLANNING SOFTWARE

2 INTRODUCTION It is an important part of any marketer s role to run campaigns that boost the performance of a brand, while measuring the success or failure of a particular campaign through key performance indicators (KPIs) such as sales, awareness or purchase consideration. To fully understand the effect of a campaign, marketers need to be able to assess the individual contributions / efficiency of each media touchpoint individually or in combination. Marketing Mix Modelling is a valuable tool for marketers to measure the effectiveness of their investments and optimize future activities. In addition, Pointlogic has developed a completely alternative solution, called Valuepoint where the modelling is based on understanding from the perspective of individual consumer and then rolling up from there to see the bigger picture. Brand Health is a result of consumer beliefs and behavior, as such we need a respondent-level approach to understand media effectiveness BRAND AWARENESS? CHARACTERISTICS OF RESPONDENT = + Age/gender Social Class Employment Status Education Household Composition ADVERTISING EXPOSURES Media composition and behaviour TV, Online, etc? When, where and how much is advertised by the brand Interview date Figure 1: As the numbers grow, we use powerful modelling techniques to tease out all the causes and effects from the hundreds of individuals we have precise data for Valuepoint is a decision support solution that helps you deliver the brand-specific insights you need, in order to optimise the media mix. It combines media plan information with consumer survey data at an individual level to get an understanding of media effectiveness and the handholds for optimizing campaign performance. This type of modelling is not just about understanding WHAT happened, but also about understanding WHY did it happen? This method is becoming an increasingly popular methodology for many businesses in need to understand media effects in great detail, as it benefits from: More data: Tracking an 8 week campaign with 150 people interviewed per week creates data points versus 8 in an aggregate world; Higher granularity: Increasing the data points provides a platform for more detailed analysis, to better identify subtle media effects; Faster: If a business already has a tracker in place, there is no need to collect fresh data; Actionable results: Speeding up the modelling process means the results can be applied immediately, before the data becomes obsolete.

3 Utilizing Bayesian modelling techniques, Valuepoint will quantify the effect that each media channel had on each KPI, determine whether or not the investment on the channel has hit diminishing returns, and deliver a recommendation for an optimised media spend that will deliver the greatest ROI. The effectiveness outputs of this modelling is loaded into Valuepoint s planning software, providing intuitive user interaction through scenario-based analyses. Clients can use this tool to build optimised cross-platform campaign allocations across all measured media channels to maximize the ROI for each campaign. It will also include channel recommendations and an optimiser, based on the planned campaign KPI goals, to improve plan recommendations. RESPONDENT LEVEL MODELLING Respondent-level modelling is a mathematical method to get a full understanding about the consumer-brand interaction. It combines media plan information with consumer survey data at an individual level to get an understanding of media effectiveness and the handhold for optimising campaign performance. Respondent-level modelling consists of two steps. First, estimating for every respondent the ads he/she was likely exposed to. Secondly, we need to understand from the individuals in the sample how being exposed to a mix of media translates to being aware of the brand or other KPIs. There are two major data sources for the process: a consumer tracking study and the media plan for the campaign being evaluated. (Figure 2) Individual s media comsumption Detailed media schedules & costs 1 KPI score for individuals from tracker Contact of individuals with campaign (estimated) 2 EFFECT OF AD ON KPI SCORE Figure 2: Illustration of the modelling process SOURCE DATA The starting point is an ad- or brand tracker that allows us to monitor how brand marketing activities are performing, with data often extracted at a weekly level. In many cases, this is done in the form of a survey with a number of respondents asked a range of questions every week that ascertain their awareness and opinion of certain brands. If 150 respondents are asked these questions every week, this means already 150 data points that can be used for modelling - a number that can take up to a year to accumulate using traditional methods. To empower modelling for a specific media mix, additional questions provide understanding of who has been exposed to certain media. For example, individuals will be asked whether they watch TV and how often (frequency & duration). More specifically, they may be surveyed on which channels they favor using a client s TV channel mix as a guide. The second input to the respondent-level modelling is a detailed overview of the realized media schedules and cost. For example, the media schedule for TV would have date of spots aired, and GRPs / TRPs per spot or ad break.

4 STEP 1: ACCOUNTING FOR THE MEDIA MIX The first step in modelling the effectiveness of media at a respondent-level is to understand who is likely to have been exposed to which medium, and how often. But before we explain how we determine these advertising exposures, we discuss why we prefer to use advertising exposures above ad recall. WHY DO WE PREFER TO USE ADVERTISING EXPOSURES? Often advertising recall is used for explaining a brand s KPIs, instead of advertising exposures. There are several reasons why using advertising exposures is preferred: Ad recall might be biased: people might recall an advertising while they have not seen it. An example from our past project experience: recall of a Television ad was included in the pre-campaign tracking. The result: 30% of the respondents said they recalled the ad in the pre phase of the campaign. The level of bias will be different by medium and also depends on how similar the current advertising is to past advertising. Ad recall already contains a form of effect: did people move from just seeing the ad to remembering it? Using advertising exposures is in that sense more objective. When using advertising exposures, we can explain what the impact is at different levels of exposures. We can take into account diminishing returns of advertising as well. Using ad exposures, we can also establish what the best advertising levels are: what is the optimal level of exposures before the diminishing returns start to become too big. When using recall, it is more difficult to give an advice on the optimal advertising levels. CALCULATING EXPOSURES WITH THE CAMPAIGN Using the individual data, an estimated number of exposures is given to each person based on the likeliness they have been exposed to a particular campaign. If it is known that 70 per cent of people were reached, these individuals need to be identified and assigned a positive indicator. While 70 per cent of individuals may have been reached in the first week, this may go up or down in the second week, making the date on which survey respondents were contacted an essential component of the modelling. The actual process is more detailed, with individuals being assigned a certain number of exposures per medium per day or week rather than a simple yes or no approach, taking into account several other elements. The following elements play a role in assigning exposures to respondents: which media are consumed, how often and how long; date of interview; rate at which ad spends and effects carry over to subsequent weeks (adstock); media plan; the total number of exposures for all respondents should match with the GRPs bought for each medium, and the net reach achieved. A representation of the logic behind the methodology of step 1 can be found in Figure 3. This illustration is somewhat simplified, as it ignores the carry over effects of spending.

5 Figure 3: Illustration of the process of assigning exposures to respondents based on their media consumption and the media budget The procedure described before is a general one. Exposure levels can also be pulled in from other sources if available. Nowadays, it is often possible to tag online content using cookies, allowing an actual measurement of exposures to ads for online, rather than an estimate. These measured exposures can then be used next to the estimated exposures for other media. The ultimate aim is to create a database with all the tracking variables and estimates for the number of exposures across the entire media mix for each individual. This database acts as the raw data for the second stage of modelling and can be linked to individual KPI targets, such as awareness, preference or purchasing intent. Matching this data with the overall media campaign data provides the insights necessary to assess the success of advertising spend. ESTIMATING THE MEDIA CONTRIBUTORS The second stage involves estimating the contribution of each part of the media mix based on different demographics isolated from the raw data, including gender, location, socioeconomic group and age. Using these demographics as a baseline, it is not only possible to examine how much each part of an advertising campaign is contributing to the total number of exposures, but also to see how this affects individual KPIs. The following elements play a role in estimating the contributions of media: estimated exposures (the result of step 1); respondent demographics; respondents attitudes and behaviour; prior knowledge on media effectiveness from other sources, e.g. TV ad effectiveness studies. A key concept in the modelling of media effectiveness is the concept of diminishing returns. When advertising, the first few ads can be very effective in getting people to change their perception of the brand. However, as a consumer is exposed to the same ad over and over, the additional return on your ad investment will diminish. Media differ in both their potential to influence consumers and the speed with which they reach their saturation point. Pointlogic models media effects including diminishing returns using an effect curve (Figure 4).

6 Figure 4: Effect curve accounts for media properties such as potential, speed and diminishing returns Media effects are isolated from each other and from the relevant demographic variables by taking into account all media simultaneously, making sure no medium holds preference above another medium. The mathematical method used for this is multivariate Bayesian modelling, which is fed by the media effect curves. Bayesian modelling is a specific econometric modelling technique which is better able to capture prior our outside knowledge compared to traditional econometric modelling. This allows for taking into account knowledge from e.g. TV ad effectiveness studies. POST-PROCESSING At Pointlogic, we don t just compile the data and create a model - we provide it in such a way that it enables our clients to quickly and accurately see where their advertising money is best spent. Our simulation and software tools outline the current marketing expenditure, but also show the optimal spend across all campaigns. Are you experiencing diminishing returns on your online advertising? Are you getting maximum value out of your TV advertising? Or should you be investing more money in direct marketing efforts? All of these questions and many more can be answered through our intuitive, easy-to-use platform, ensuring businesses can identify and target the best marketing mix for their goods and services. This software is provided to all stakeholders at our clients. This software also allows comparing and contrasting different advertising spend levels to see what provides maximum value against KPIs, while also having the option to input various budget guidelines to see whether there is a significant improvement. This helps businesses not only evaluate the success of their current campaigns, but also enhance their ability to effectively plan future scenarios. The remainder of this document contains screenshots and highlights of the analyses a business will be able to run using our software based on the modelling results.

7 SAMPLE SCREENS FROM VALUEPOINT PLANNING TOOL Campaign KPI mix selection Once the user selects the nameplate and model to use in the software, they can begin running scenarios to help determine the optimal media scenario for future campaigns. The user can then select individual KPIs to optimize against, or select multiple KPIs and weight them according to importance. The weighted KPI scores are used later in the software as objectives for media optimization. Unique channel effect curves for each KPI The user can review how each KPI s effect builds and reaches its potential by media channel and creative tracked in the survey.

8 Plan builder with optimizer showing expected KPI lift The user can create an unlimited number of scenarios in the software by manually entering a plan (for example last year s campaign) to see how it performs. They can understand the base level of each KPI as well as the lift in KPI attributable to the campaign. The user can also use the Add Plan wizard to build an optimal plan that takes into account the KPI mix set up previously in the software, the effect that each channel has on the KPI, as well as the cost of each channel. In addition, the user can set constraints on the optimizer to put minimum and maximum allocations for each channel, if there are any restriction needed for that scenario. The key benefit of this function is to give the user an understanding of the projected impact that shifting budgets will have on their KPIs. Some examples include: If the budget needs to be cut by 20%, exactly how much less lift do we expect to see in each KPI If there is no OOH creative so we don t use OOH, where should we reinvest those dollars for maximum ROI If we consider TV to be fixed at a certain spend level for this campaign, which channels are most effective at driving the KPIs alongside of TV The user can do a deep dive into plan performance and explore how each media channel delivers against the KPIs.

9 The user can also easily compare the performance of each plan against the others to help determine which plain is most suitable for the campaign. META-MODELS Once we analyze a number of campaigns, we will be able to generate additional insights by creating meta-models from the individual brand and campaign models. This additional level of analytics can help companies to understand the impact of additional variables on campaign performance such as elements of brand and category (life stage, competition) and creative (presence of music, VO, action shots, star talent, etc.). These insights would be accessed in the software through a Q&A screen that would allow the user to customize the model output further to make it more relevant to the campaign. SUMMARY The Valuepoint solution will leverage the disparate data sources you are collecting and combine them into one extremely powerful planning tool that will deliver an easy to use strategic planning solution to deliver the most impactful plan for the media budget. Beyond helping to identify the most impactful media plans for each unique campaign and brand, it will provide a host of additional benefits: Provide campaign effectiveness insights by brand, campaign, and channel to better understand media utilisation based on impact and not just on R/F and costs; Allow the users to explore multiple planning scenarios for a campaign to understand the impact of shifting budgets across channels as well as impact of decreased budgets or value increased budgets can bring to the KPIs; Provide the granular analysis needed to understand how a campaign and channel is performing to understand whether to adjust budgets, allocations across media, or creative selection; Deliver an enterprise, scalable solution to help in the creative development and media planning process.

10 CASE STUDY ESPN has been a Pointlogic client for many years and is using our services to help them prove ROI to their top 26 advertisers. They use Pointlogic s Valuepoint planning software, powered by the econometric models we have built, to optimize media plans for their clients across their many media platforms including linear TV, non-linear video, display, print, mobile, tablet and more. Please see this short presentation given by ESPN at the most recent ARF conference for a case study of Pointlogic s ability to aggregate numerous data sources across many channels to help ESPN deliver the greatest ROI to their advertisers: MARCEL VAN DER KOOI EVP Global Business kooi@pointlogic.com