Big Data Insights Reveal MSO Marketing Effectiveness

Transcription

1 Big Data Insights Reveal MSO Marketing Effectiveness First of its kind industry research involving more than 10 billion anonymous cable TV subscriber and viewing behavior data points, fused with atomized ad insertion data and related metadata for 144 cross-channel marketing campaigns conducted over 18 months.

2 Big Data Insights Reveal MSO Marketing Effectiveness Contents Introduction 3 Background 3 Research Data 3 Methodology 4 Data Fusion 4 Viewer Validation 5 Exposure Attribution 5 On-Target vs. Off-Target 5 Conversion Attribution 5 Key Research Findings 6 TV Viewer Behavior 6 Subscriber Product Penetration 7 Cross-Channel Marketing Campaigns 10 A. How TV Viewing Affects Campaigns 10 B. The Realities of Reach & Frequency 11 C. On-Target vs. Off-Target Impressions 12 D. Targeting Impact on Conversion 12 Enhancing Cross-Channel Marketing Effectiveness 14 1) Behavioral Segmentation 14 2) Household Addressable TV Advertising 15 3) Frequency Capping 15 Closing Thoughts 17 Research Team 17 2

3 Introduction Background Just about any Chief Marketing Officer (CMO) who works for a consumer goods company would love to have a communication channel as powerful as television, able to directly reach its customers and at their disposition every day without any direct costs (but material opportunity costs). Well, CMOs leading marketing operations for major cable TV operators, or as they are known in the industry, multiple system operators (MSOs), enjoy that enviable position. Most MSO marketers have at their disposal what they commonly refer to as their cross-channel marketing TV ad inventory. This proprietary marketing channel is typically made up of 25-35% of the total local cable TV ad inventory that MSOs own as part of their carriage agreements with the national cable TV networks. The volume of cross-channel marketing inventory available to MSO marketers is large enough that it substantially overshadows all other marketing communications used by MSO marketers (including direct mail, , Internet, telemarketing, etc.). The feeling among MSO marketers is that the sheer volume of cross-marketing efforts makes it effective. When probed, the primary challenge MSO marketers highlight is that of keeping up with the volume of campaigns and commercials needed to fulfill the ad inventory allocated for cross-channel marketing efforts. One of the CMOs characterized the magnitude of this challenge as feed the beast. Given the heavy day-to-day operational focus with getting campaigns on-air, the present reality is that MSO marketers have minimal understanding as to how well their cross-channel marketing efforts work. Some MSOs do use competitive intelligence information and/or sales related data to determine whether or when to change campaigns. Nevertheless, there is a general lack of understanding as to what drives the effectiveness of cross-channel marketing campaigns. It is in this context that a top tier MSO agreed that it would be worthwhile to conduct a major research initiative to derive insights regarding the effectiveness of their cross-channel marketing efforts. Over the course of nine months, Visible World and b3 Intelligence combined their expertise to execute the research. Visible World is the leading provider of targeted TV advertising technology and campaign management applications used by top tier MSO marketing groups. b3 Intelligence is a research firm (founded by former senior executives from ComScore) that specializes in guided analysis of large data sets to help uncover meaningful marketing insights. The stated objectives of the research were to better understand the overall effectiveness of cross-channel marketing campaigns, as well as to identify and quantify potential opportunities to improve related marketing efforts. Research Data To enable the research to happen, the MSO initiated a series of data requests from various internal sources and several external vendors. The research data compiled included months of detailed information for every subscriber household, as well as viewing data derived directly from each related set-top-box (STB). It is important to note that no personally identifiable information (such as name, address, phone, , etc.) was provided. In addition, the research included detailed information for 144 cross-channel marketing campaigns conducted over 18 months. 3

4 The specific research data gathered included: 1) Subscriber data: Product(s)/Service(s) portfolio Money spent per month Tenure (overall and by product) Demographics (including segmentation) Geographic (region, market, cable zone) 2) Viewing data (derived from set-top-box): Time spent viewing TV Total number of TV networks viewed Number of unique TV networks viewed Time spent viewing TV by day-part Time spent viewing TV by network Time spent viewing TV from a DVR 3) Cross-channel marketing campaign data: Start- and end-dates of campaigns Metadata for each campaign (e.g. creative, product, offer, etc.) Campaign geography (region, market, cable zone) Ad insertion (network, date, time, cable zone) The data provided by this top tier MSO was quite substantial. The research examined 13,346,280 subscriber records and 10,342,499,269 viewing records, which were fused with 57,929,300 cross-channel marketing campaign records. Since one of the requirements from the MSO that provided the research data was to maintain their anonymity, the research results presented here do not include any reference to information that could be used to determine the identity of the MSO. Consequently, references to specific geographies or campaign information have been anonymized, and subscriber counts have been represented as percentages relative to the population or subset analyzed, so as to avoid revealing information that could be use to infer the identity of the MSO who provided the research data. Methodology For the most part, the biggest effort associated with this research into cross-channel marketing effectiveness was the compilation, fusion and management of very large data sets. From an analytic perspective, the research involved fairly basic, standard statistical methods and measures. Due to the large number of data points involved, even small differences in the results are associated with high level of statistical significance (at least 98% confidence level). And that is certainly the case for all of the findings presented in this white paper. Data Fusion To attribute viewership of cross-channel campaigns to specific anonymized subscriber households, the set-top-box data associated with each subscriber household was linked to the atomized campaign data. Namely, when the geography, network and time of any given ad insertion corresponded to the network, date, time, and geography associated with a specific STB, it was considered as an opportunity for the related message to be exposed to the subscriber household. 4

5 Viewer Validation One of the challenges associated with doing research using STB data is that because a TV set can be readily turned on and off, whereas a set-top-box takes time to reboot when turned on, most consumers prefer to turn on and off their TV set, while leaving on their set-top-box. As such, it is very important to properly filter the STB data to increase the likelihood that a subscriber household was indeed exposed to any given cross-channel campaign message. The STB data used in this research study identified a number of user operations that affect the set-top-box, including TV channel or volume changes. For the purpose of this research study, the STB data was analyzed to determine whether there was recent user activity. This research study used a last usage activity of less than 1 hour to attribute any given ad exposure to any specific subscriber household. While it is recognized that this is an arbitrary and somewhat strict condition, what is most important comes it that it was consistently applied throughout the research. Exposure Attribution Two conditions were deemed necessary for a campaign exposure to be attributed to a specific subscriber household: 1) There was usage activity on the set-top-box within the past hour; and 2) The set-top-box was tuned to the network at the time there was a specific cross-channel campaign ad insertion. On-Target vs. Off-Target For the purpose of this research, a cross-channel campaign message was deemed to be on-target if it featured a product the subscriber did not already have. For example, if a household did not already subscribe to high-speed Internet service, then a campaign message offering the product was deemed to be on-target. On the other hand, if a campaign featured one or more products to which the household already subscribed, then it was deemed to be off-target. For example, any household that already subscribes to the MSO s Triple Play (TV, Internet and Phone) was deemed to be off-target for campaigns featuring any of those products. While there are some valid reasons to have campaigns focused on current users of a product, such as reinforcing a purchase decision or highlighting its ongoing value, the research decision was made to consistently apply the described on/off-target criteria to standardize related measurement. The reason behind this decision is that it is fairly common for cross-channel marketing campaigns include promotional offers. Therefore, it was deemed important to get a better sense as to what extent subscribers were being exposed to the MSO s cross-channel marketing campaigns that may feature offers for products to which they already subscribe. Conversion Attribution Because of the large scale and longitudinal nature (18 months) of the research data, it was possible to examine changes in the product portfolio of subscriber households, and this information was used to assess the extent to which crosschannel marketing campaigns affect changes in the subscriber product portfolio. For the purpose of this study, a cross-channel marketing campaign was associated with a positive effect on subscriber value, if the subscriber household added the product featured in the campaign. Both overall conversion, any time after the related campaign exposure, and conversion within 90 days from exposure to the campaign, were examined in the research (However, the results presented in this white paper are limited to conversion within 90 days from campaign exposure). 5

6 Key Research Findings To date, the compiled research data has been used to derive insights related to TV viewing behaviors, subscriber product portfolio, tenure and value, subscriber churn (customer retention), and cross-channel marketing effectiveness. In addition, there has been interest expressed in analyzing the data to better understanding the value of TV programming relative to subscriber consumption and value. In this white paper, discussion will be limited to research findings that have the greatest impact on the effectiveness of cross-channel marketing. TV Viewing Behavior Most major consumer goods marketers are aware that U.S. households spend a large amount of time watching television. Nielsen s latest Cross-Platform Report (3Q 12), reported that Americans spend more than 34 hours per week in front of a TV set. Table 1: Subscriber TV Viewing Behavior For the purpose of this research, the goal was to examine TV viewing behavior of the MSO subscribers as a basis to understand potential impact on cross-channel marketing effectiveness. As Table 1 illustrates, when the subscriber base is divided into five groups based on the amount of daily time spent watching TV, there are substantial differences across the quintiles. The Top Quintile accounts for 45% of all TV viewing, and the top two quintiles together account for 69% of all TV viewing. These results are closely aligned with findings in the cited Nielsen study, as it reports that the TV viewing quintile analysis revealed that the top and top two quintiles account for 47% and 72% of all TV viewing respectively. As Table 2 illustrates, the importance of Prime Time is clear as it represents the greatest percent time spent viewing TV across every one of the TV viewing quintiles. However, it is notable that as a percentage of time spent watching TV, the Bottom Quintile watches more Late Night and Overnight than other quintiles. And, the Top Quintile spends more of their time watching Daytime than other quintiles. Table 2: Subscriber TV Viewing Behavior by Day-Part 6

7 As part of the subscriber data obtained for the research, most of the subscriber households had a related Nielsen Claritas ConneXions segment. The ConneXions segmentation aggregates and classifies Claritas PRIZM clusters into ten segments based on the combination of two key variables: Life Stage and Affluence. Life Stages are characterized as Young (Y), Family (F) and Mature (M), whereas Affluence is represented in four groups ranging from Highest to Lowest, where 1 represents the most affluent group and 4 represents the least affluent group. The MSO marketer involved with the research was particularly interested better understanding cross-channel campaign performance for the ConneXions segments, which were initially used for direct mail campaigns. There was growing concern that this segmentation was not delivering the desired business impact. Examination of the TV viewing behavior for the ConneXions segments served as a starting point. As Table 3 illustrates, affluent technology adopter segments (Y1, F1 and M1) spend between 17-24% less time watching TV than overall subscribers. On the other hand, the least affluent, mature segments (M3 and M4) index 7% higher than the overall average. However, because these two segments represent nearly 47% of the MSO s subscriber households, altogether they account for 48% of all TV viewing. The key take-away from the analysis of TV viewing behavior is that the high concentration of TV viewing makes it inherently challenging for marketers to reach their desired target audiences (unless the target is mature households). It is worth noting that Simulmedia ( recently enabled public access to a web-based reporting tool that illustrates campaign reach and frequency for major U.S. consumer brands. Simulmedia s analysis uses STB data from millions of U.S. households, and it confirms a very high concentration of TV ad impressions among heavy viewers of TV and illustrates the related impact on reach and frequency. Table 3: Subscriber TV Viewing Behavior by Segment Subscriber Product Penetration Examination of the product uptake across ConneXions segments provides insights as to the relative affinity between products and segments. Note that the analysis focused on the MSO s three most important product: TV, Internet and Phone, as these products and their related bundles accounted for more 89% of the MSO s subscriber revenue. 7

8 Table 4: Subscriber Product Penetration by Segment As Table 4 details, the research revealed that younger, more affluent ConneXions segments (Y1 and Y2) are at least 50% more likely to have the TV & Internet product bundle than Triple Play that includes the Phone product. Most notable, more than half of the Young & Wireless (Y1) segment has the TV & Internet product bundle, which is 65% more likely than all segments. And, the Emerging Techies (Y2) segment is 30% less likely to have the Triple Play product bundle, and represents the lowest Triple Play penetration across all segments. At the other end of the socioeconomic spectrum, the least affluent, mature segment (M4) is twice more likely to have the TV & Phone product bundle than all ConneXions segments. Nevertheless, Triple Play remains the most popular offering for the M4 segment, as it is the bundle of choice for nearly 48% of the segment. It is important to note that overall product penetration is high across all segments. Overall Internet product penetration is lowest among Elderly Traditionalists (M4) but it is still over 70%. Adoption of the Phone product is lowest among Young & Wireless (Y1) and Emerging Techies (Y2), both at roughly 41% penetration, but overall penetration of the Phone product is more than 53% of all subscribers. As previously mentioned, the MSO involved with this research initially used the Nielsen Claritas ConneXions segmentation in its direct mail marketing efforts. The decision was subsequently made to use the segmentation with their cross-channel marketing efforts. Unlike direct mail where households can be individually targeted, this MSO does not currently have the ability to target TV ads to individual subscriber households. However, this MSO marketer does have the ability to target TV ads to individual cable zones (which individually typically represent from a few to a dozen or more zip codes). However, during the period covered in the research, the great majority of its cross-channel marketing campaigns were more broadly executed at a market or even region level. Given that this MSO marketer does have the ability to target its cross-channel marketing campaigns to individual cable zones, the research sought to better understand the ConneXions segment composition at the cable zone level. As Table 5 illustrates, the summary statistics across cable zones served by this MSO make it clear that the cable zone level ConneXions segment composition is quite diverse. 8

9 A detailed examination of individual cable zones further confirms that with the exception of a few smaller, more affluent cable zones, most cable zones are likely to be too diverse to significantly benefit from targeting with ConneXions segment specific messaging. Table 5: Subscriber Segment Composition across Cable Zones While understanding the affinity between products and segments is interesting, it is even more important to take into account the penetration of products across the geographies where they are available. Understanding product penetration provides insights as to the relative affinity between a product and any given geography, and also helps estimate relative levels of opportunity or saturation across geographies. The summary statistics in Table 6 show that product penetration levels are relatively high across cable zones served by this MSO. Nevertheless, the derived cable zone level penetration average, median, mode and standard deviation for Internet and Phone products indicate some level of variance across zones. An individual analysis of the MSO served cable zones indicates that 37% cable zones are underpenetrated by at least 10% relative to the mode penetration for Internet. Table 6: Subscriber Product Penetration across Cable Zones The outlined findings for product penetration within the ConneXions segments and across cable zones highlight a significant challenge for MSO marketers. Relatively high levels of product penetration make it increasingly more difficult to cross-sell products into the existing subscriber base. Moreover, it is difficult to target cross-sell messages and offers to attract incremental business from existing subscribers without also exposing households who already subscribe to those products. 9

10 Cross-Channel Marketing Campaigns Examination of the product uptake across ConneXions segments provides insights as to the relative affinity between products and segments. Note that the analysis focused on the MSO s three most important product: TV, Internet and Phone, as these products and their related bundles accounted for more 89% of the MSO s subscriber revenue. Most MSO marketers work hard trying to keep up with the operational demands of having to fulfill the available cross-channel marketing inventory with an ongoing stream of campaigns. When it comes to measurement, some MSOs do track related website visits, calls, or sales associated with specific campaigns, but relatively little has been done to better understand and quantify the actual reach, frequency, and effectiveness of cross-channel marketing efforts. To be fair, until recently it would have been nearly impossible to achieve such a clear picture of cross-channel marketing effectiveness. The ability to collect vast amounts of STB data, and the advent of analytic processing power and tools, now makes it possible to shed scientific light into this previously unobserved territory. As such, an explicit purpose of this big data research initiative was to quantify the effectiveness of cross-channel marketing campaigns, and in doing so, yield insights that could help drive greater value from this important MSO marketing resource. A. How TV Viewing Affects Campaigns In media planning, impressions are calculated based on an estimate of the people that are potentially exposed to a campaign and the estimated frequency in which they see the ads. What has recently changed is that instead of estimating impressions, it is now possible to actually match campaign ad insertion and individual viewing (STB) data to identify actual exposures, and in doing so, better understand how much viewers are exposed to each campaign. As can be expected, the amount of time subscribers spend viewing TV largely determines the likelihood of them being exposed to any given TV ad campaign, and this was definitely the case for the 144 cross-channel marketing campaigns that were examined as part of this research effort. As Table 7 illustrates, the top two quintiles in terms of time spent viewing TV accounted for 80% of the many millions of campaign impressions (purposely unclear to protect the identity of the MSO). This clearly indicates that heavy TV viewers are far more likely to be exposed to cross-channel marketing campaigns. Table 7: Campaign Impressions by Subscriber Time Spent Viewing TV 10

11 This finding is important as most MSO marketers generally assume that because cross-channel marketing campaigns are on TV on a regular and abundant basis, they are sure to be seen by practically all subscribers. This is clearly not the case. B. The Realities of Reach & Frequency Use of reach and frequency are common in media planning. Reach being the total number of people or households exposed to campaign ads over a given period of time or campaign cycle, and frequency being the number of exposures to the same message each household supposedly receives. Average frequency is typically estimated as the total exposures for all households reached, divided by the number of households reached by the campaign. The concepts of effective reach and frequency, which take into account the number of exposures needed for a campaign to have the desired effect, are also common planning objectives. As previously highlighted, most of the cross-channel marketing impressions are seen by heavy TV viewers. As such, it is critical to recognize that an average frequency of exposure has relatively little resemblance as to just how often most viewers are exposed to a campaign. In the case of the 144 cross-channel marketing campaigns analyzed, the average frequency across subscriber households reached by a campaign was 2.7 times. However, as the chart on Figure A shows, the distribution of exposures paints quite a different picture. Specifically, the research revealed that reaching an adequate level of effective frequency of campaign exposure is a major issue, as over 50% of subscribers reached are on average exposed only to a single campaign exposure. On the other hand, 4% of subscribers were on average exposed at least 10 times and some many more times to each campaign, and together this small subset of subscribers accounted for more than 60% of all of the campaign exposures. Figure A: Average Frequency of Campaign Exposure Being able to quantify the frequency distribution of exposures provides ample evidence that most subscribers are not likely to be sufficiently exposed to any given cross-channel marketing campaign. And at the other end, heavy TV viewing subscribers are overexposed to cross-channel marketing campaigns, well beyond what is likely to be a practical level of effective frequency, and hence, these represent a significant amount of largely wasted impressions. 11

12 C. On-Target vs. Off-Target Impressions In addition to helping quantify, and in doing so improve measures of reach and frequency, the research study also used the matching of subscriber, viewing and campaign data records to better understand to what extent cross-channel marketing campaign ads were on-target or off-target. As described in the methodology portion of this white paper, a cross-channel campaign message was considered to be on-target if it featured a product the subscriber did not already have, and otherwise was deemed to be off-target. It is also important to note that the research study focused on cross-channel marketing campaigns that featured the MSO s highest revenue producing products (TV, Internet and Phone) and related bundles (which account for 89% of total subscriber revenues). The analysis also excluded campaigns exclusively focused on TV related offerings such as video-on-demand or premium channels. As such, the focus was exclusively on cross-sell of Internet, Phone, and related Double or Triple Play bundles to digital TV subscribers. (Analog TV subscribers are not nearly as numerous and were not included because they do not generate the STB data necessary to match TV viewing to campaign ad insertions.) As summarized on Table 8 below, across the all of the relevant campaigns and related many millions of impressions analyzed as part of this study, just under half of all campaign impressions were deemed to be on-target. Looking at these results, it is hard not to recall John Wannamaker s famous quote: Half the money I spend on advertising is wasted; the trouble is I don t know which half. Except that with the detailed data available for this study, it was possible to identify the factors causing impressions to be off-target. Table 8: Percent On-Target Impressions by Campaign Focus As outlined in the prior section on Subscriber Product Penetration, overall Internet and Phone product penetration are quite high across all segments. Although there is some variance in Internet and Phone penetration across cable zones, the level of product penetration across most cable zones is relatively high. Consequently, cross-channel marketing campaigns that aim to cross-sell Internet, Phone or related bundles, by definition end up exposing households who already subscribe to those products. Another significant factor is that Internet and Phone product penetration are very high among the less affluent mature ConneXions segments, who also account for 48% of all time spent viewing TV. Internet and Phone penetration among these two ConneXions segments is 75% and 61% respectively. D. Targeting Impact on Conversion Major consumer goods marketers realize that product adoption depends on many factors including the attractiveness of the products and related pricing, suitable exposure to marketing messages, as well as competitive environment. MSO marketers benefit from the strengths of their products relative to their competition (with perhaps the exception of markets where they face competition from Telco TV offerings). As such, the performance of cross-channel marketing campaigns plays a major role in driving MSO business results. 12

13 As Figure B below illustrates, across the 144 campaigns examined as part of this research study, overall conversion levels ranged from nearly zero to a high of just under 8%, meaning that that nearly 8% of the subscribers exposed to that particular campaign acquired the featured product within 90 days of their last exposure to the campaign. Figure B: Campaign Conversion Overall & On-Target Impressions The ability to link STB, subscriber and campaign data to understand whether cross-channel campaigns are on-target helped reveal an important finding. While conversion rates varied greatly across campaigns, conversion results for on-target subscribers were on average 70% higher than across all subscribers reached by each campaign. More detailed analysis of the cross-channel campaigns provided further insights as to conversion levels across various products, segments and geographies, as well as conversion performance for different creative, offers, and calls-to-action. One of the more important findings of the conversion analysis for cross-channel marketing campaigns is clear evidence of a direct relationship between the frequency of exposure of on-target impressions and campaign conversion rates. It should be noted that this particular analysis focused on the 70 cross-channel marketing campaigns with the highest overall number of on-target impressions. As Figure C highlights, from the Bottom to the 2nd highest time spent viewing TV quintile, conversion rates increase consistently with higher frequency of on-target campaign impressions. However, it is notable that for the Top TV viewing quintile, average campaign conversion declines despite reaching a substantially higher frequency of ontarget impressions. Figure C: Campaign Conversion by Frequency of On-Target Impressions 13

14 The ability to link subscriber, viewing and campaigns data to quantify the conversion impact of the frequency on-target impressions provides compelling evidence that cross-channel marketing campaigns generally benefit from increased exposure to appropriately targeted subscribers. For this MSO, the average optimal level of campaign frequency was five on-target exposures. Beyond the optimal level of message frequency, incremental on-target impressions are less likely to be of value and can even be counterproductive. The findings derived from this first of a kind industry big data research effort identified several major challenges that MSO marketers face in trying to get the most out of their cross-channel marketing campaigns. Nevertheless, the ability to better understand and quantify the actual reach, frequency, and effectiveness of cross-channel marketing efforts also revealed important insights that can serve as a call-to-action for MSO marketers that seek to reduce inefficient use of cross-channel marketing TV ad inventory and drive substantially better business results. Enhancing Cross-Channel Marketing Effectiveness Based on the detailed data and analysis involved in this research effort, there are three major opportunities for MSO marketers to enhance the effectiveness of their cross-channel marketing campaigns. These are outlined below: 1. Behavioral Segmentation Although targeting specific ConneXions segments at a cable zone level may not be worthwhile, using a combination of cable zone, day-part and network targeting may prove to be more effective. Testing such an approach could serve to validate and quantify its potential business impact. However, such an approach is most likely to deliver only marginal improvement. Another possibility is using a segmentation scheme substantially different from ConneXions. Given the availability of detailed subscriber and viewing data, it is possible to develop a proprietary segmentation scheme that utilizes this data. Moreover, such a segmentation scheme could be designed to take into account the combined use of network, day-part and cable zone level TV ad targeting. Even ahead of developing a proprietary behavioral segmentation based on an MSO s subscriber and viewing data, it is possible to envision why this type of segmentation could enhance the ability to deliver more on-target impressions. Given that such a behavioral segmentation includes specific subscriber product and time-spent viewing information, clusters of subscribers with like behaviors could be derived to achieve greater homogeneity of subscribers within segments (and greater differences across segments). It is also likely that segments could be differentiated both in terms of product subscription and time spent viewing TV. A practical benefit of using a behavioral segmentation that takes into account time spent viewing TV is that campaigns aimed at segments with high TV viewing could be on-air less time (and still achieve levels of effective frequency). Another possibility would be to have campaigns that target heavy TV viewers be more focused around Daytime programming, while campaigns targeting lighter TV viewers could air on Prime Time, Late Night and Overnight. In doing so, higher levels of frequency could be achieved with lighter viewing TV segments. 14

15 A proprietary behavioral segmentation used in conjunction with network, day-part and cable zone TV ad targeting, can help reduce off-target impressions for crosschannel marketing campaigns. Nevertheless, as campaign optimization would still be based on making the best possible choice across hundreds or thousands of subscriber households, use of a behavioral segmentation within the current targeting constraints would not deliver optimal business impact. 2. Household Addressable TV Advertising Based on the research, 30-70% of all cross-channel marketing impressions are off-target, which represents far less than optimal use of cross-channel media, and negatively impacts conversion rates. It is critical to recognize that the optimal solution for more efficient and effective use of cross-channel marketing can only be achieved with the ability to target individual subscriber households based on their specific product and viewing behavior attributes. Use of household addressable TV advertising for cross-channel marketing campaigns has validated that it is possible to target ads to individual subscriber households. This means that MSO marketers can avoid having Triple Play offers from being seen by current Triple Play subscribers, and in doing so, maximize on-target impressions, and reduce subscriber frustration related to promotion pricing. And as the research revealed, being able to exclusively deliver on-target impressions should on average increase campaign conversion rates by 70%. Beyond helping achieve a dramatic lift in average campaign conversion rates, household addressable TV ads can also be used to further enhance conversion rates, as well as product profitability. Targeting the right product to appropriate subscribers is a start, but these can also be sub-segmented to better target them with more effective and/or value-building messages. For example, messages to more affluent, lighter viewing TV households can focus on benefits such as convenience and flexibility, whereas messages to less affluent, heavy viewing TV households can emphasize the great value that a particular offering represents. In addition, since much of the value derived from watching TV comes from the programming itself, household addressable TV advertising could also be used to more effectively target TV program promotion based on subscriber households TV viewing behaviors. Doing so would serve to better align the promoted content with the value perceived and derived by subscriber households. 3. Frequency Capping Using household addressable TV advertising makes it is possible to anonymously track of how many times each subscriber household has been exposed to any given TV commercial, and in doing so, provides MSO marketers with the opportunity to better utilize a significant amount of cross-channel marketing impressions, as well as a very powerful tool to drive higher conversion rates. As previously outlined, the MSO cross-channel marketing research found that the top TV viewing quintile accounted for 58% of cross-channel marketing campaign exposures. In addition, the research also revealed that optimal campaign conversion rates were achieved with an average campaign frequency of five on-target impressions. Yet, the Top TV viewing quintile was on average exposed to nearly 7.5 on-target impressions (which resulted in a small negative effect on average campaign conversion rates). As such, it is expected that the ability to cap frequency of exposure among subscribers that have been sufficiently exposed to the campaign message would reduce overexposure without affecting on average campaign conversion rates among the targeted subscribers. 15

16 Figure D below, illustrates that on average, use of frequency capping could reduce Top viewing quintile overexposure by approximately 32%. Instead of overexposing these subscribers with ads they have already seen enough times, the related ad impressions can then be used to market other products/services, support brand or subscriber loyalty building messages, or even be sold for use by other advertisers (and hence, produce incremental revenue for the MSO). Figure D: Impact of Frequency Capping on Campaign Over-Exposure While avoiding overexposure presents a significant opportunity in improving cross-channel marketing media efficiency and/or making better use of related ad impressions, frequency capping can serve an even more valuable purpose. Simply stated, once MSO marketers are able to use frequency capping to avoid overexposure of campaigns, they gain the power to keep campaigns on-air longer or give them more cross-channel media weight, so that these campaigns can achieve higher levels of on-target impressions among lower TV viewing segments. And in doing so, greatly improve on campaign conversion rates. As Figure E shows, the ability to use frequency capping to reduce overexposure and enable greater on-target exposure among lower viewing quintiles can be expected to increase on cross-channel campaign conversions rates by an average of 25%. Figure E: Impact of Frequency Capping on Campaign Conversion 16

17 It is also interesting to note that relative to what most MSO marketers currently consider one of their biggest challenges, the ability to get cross-channel campaigns out the door, the opportunity to use frequency capping to keep campaigns on-air longer, should help reduce these operational demands. Closing Thoughts The combination of highly granular subscriber, viewing and campaign data, and detailed analysis involved in this industry ground breaking research effort, delivered abundant insights. Some findings validated established perceptions, while others helped identify and quantify major challenges facing MSO marketers. Most notable, the research findings imply that the major challenges facing MSO marketers looking to substantially impact results associated with cross-channel marketing campaigns require far more than incremental tweaking of campaigns. The good news is that the availability of detailed subscriber, viewing and campaign data, along with the deployment of addressable TV advertising applications, can be effectively combined to reshape cross-channel marketing efforts. And some MSO marketers have already begun to implement and leverage these capabilities. While this research focused exclusively MSO marketers use of their owned crosschannel marketing TV ad inventory, some of the major findings are applicable to most consumer goods marketers. In particular, the dramatic impact of time spent viewing TV on actual campaign reach and frequency, is clearly a broader issue for TV advertising. Also relevant, the opportunities outlined for MSO marketers to drive greater effectiveness of their cross-channel marketing efforts, represent a critical part of an industry solution to greatly enhance the overall value of TV advertising. Research Team b3 Intelligence b3 Intelligence offers data analytics, business intelligence and integrated research services. The b3 Intelligence leadership team has a strong background in high-end analytics specialized data-driven marketing research (whose founding members were previously senior research and operations executives at ComScore), and uses this deep expertise to help companies obtain the in-depth answers they need to improve their marketing decisions, processes and ROI. Visible World Visible World is the leading developer of targeted TV advertising technology and applications. Visible World has been working MSO Marketing groups for nearly a decade, and has been directly involved in the execution, tracking and measurement of both cable zone and household targeted TV advertising campaigns. In January 2012, Visible World formally established a research function to leverage its expertise in analyzing the efficiency and effectiveness of targeted TV advertising campaigns, its extensive experience working with set-top-box data, various types of consumer data available from third parties including Acxiom, Experian, Nielsen, and others, as well as client-specific data sets. 17

18 About Founded in 2000, Visible World is the leading provider of targeted television advertising solutions. The company s suite of suite of technology, applications and services enables advertisers, agencies, and media companies to deliver addressable, measurable ads. Today, Visible World empowers over 300 advertisers to deliver more effective and more accountable campaigns to consumers in approximately 80% of US cable homes. 460 West 34th Street, 14th Floor New York, NY p: Visible World Inc. All rights reserved.