Making It Real: The State of Trade Promotion Management

The Readiness is Not All
Acknowledging the fact that trade promotion is a critical aspect of the consumer packaged goods business is not an issue among respondents. More than two-thirds (69%) say their company considers the practice "extremely important," and nearly all (94%) ascribe some level of importance.

However, survey results overall point to dissatisfaction with existing systems, general disapproval over program performance, and even some professed ignorance about general effectiveness, as will be discussed below. While there is widespread understanding that trade promotion is vital, many companies have hesitated to make necessary changes to their processes.

For instance, not a single respondent to the survey could say that they were "completely satisfied" with the quality of performance analytics they can access on a daily basis; 63% say they are "somewhat satisfied," while 37% expressed dissatisfaction.

When it comes to improving performance, respondents pointed overwhelmingly (67%) to the basic need to improve promotion planning and optimization as the single most important area of focus. Coming in a distant second was the need to boost program execution at retail, an achievement that has long proved troublesome for CPGs at the mercy of store employees. Enhanced data and insights, an enterprise-wide goal at most companies, was third (Figure 1).

It might also be noteworthy — and certainly goods news for legacy TPM providers — that only 3% consider the need to replace technology solutions as the most important area to cover.

That's somewhat surprising, considering the stated level of trade promotion optimization technology that respondent companies have. Only 11% consider their tools to be "highly advanced," with the ability to leverage multiple, internal and external data sources using artificial intelligence to predict performance (Figure 3).

Another 28% believe themselves to be "advanced," with the ability to use historical, point-of-sale and syndicated data to deliver promotion recommendations (without the use of AI).

But that leaves 42% of companies still operating with only "basic" capabilities, making predictions using historical data but utilizing no other resources. And even more concerning are the 17% of companies that still aren't using a TPO tool.

All About the Data
Drilling deeper down into data and insights, respondents were somewhat divided on what type of data is most important for improving performance. The most obvious source was the one cited most often, with "accurate" historical performance results being selected by one-third of respondents (Figure 2). Syndicated research data (from usual suspects like IRI, Nielsen, GfK and NPD) was selected by 19%, while one-fourth of respondents expressed a desire to move beyond more traditional streams, selecting consumer demand/shopper intelligence as most important.

Historical and POS data are also considered to be most important for optimizing ROI forecasting, with 56% of respondents pointing to both as "most critical" to the process. But nearly one-third (31%) identify data from relevant consumer promotions and events, and 22% call for syndicated data — both of which again illustrate efforts to broaden the range of insights that drive the planning process (Figure 4).

This goal is also evident in industry-wide efforts to coordinate trade promotions with other consumer marketing activity, including both corporate events and focused consumer promotions. Results in this area were fairly positive, with all respondents claiming at least some level of alignment, 14% even professing to be "always aligned" and only an equal 14% saying they "rarely" coordinate activity (Figure 5).

Respondents overwhelmingly (82%) support the idea that integrating consumer promotion event data into trade planning would improve ROI, with 24% believing it would "dramatically increase" results and 58% projecting a more marginal impact (Figure 12).

Correspondingly, data from digital consumer promotions (64%) and corporate advertising schedules (18%) are viewed as the elements that would be most effective to optimize ROI. Going beyond company-specific data, 15% of respondents also point to social media sentiment as beneficial (Figure 13).

Addressing Out of Stocks
Naturally, the weaker the forecasting process, the greater the chance of experiencing out of stocks during an event, an issue that is still prevalent for all CPG companies. Responses are somewhat mixed in this area, with 57% saying it happens less than 33% of the time but a surprising 31% saying they don't have any visibility into the frequency (see "Out of Stock Frequency," above).

However, respondents were more likely to blame these OOS situations on outside forces than on their own planning: 47% say that the leading cause is poor in-store practices on the part of retailers and another 18% pointing to failures in retail execution or store audits (see "Out of Stock Causes," above).

Alleviating the critical issue of OOS situations could be a major benefit of a move toward daily measurement of trade promotion performance, a capability that is becoming increasingly possible for many consumer goods companies. Nearly half (46%) of respondents say that daily tracking could "potentially end" out of stocks (Figure 6).

The greatest benefit, however, would be the ability to arm retailers with better intelligence about store traffic, basket makeup, and shopping behavior that, in turn, would give CPGs richer planning intelligence, according to respondents. It also would help CPGs align trade and consumer activity, they say.

Better intelligence also can be influenced by retailers who collaborate more openly in the planning process (according to 40% of respondents), improve program compliance in stores (26%) and provide daily inventory data (20%), respondents say (Figure 7).

Efforts to Improve Measurement
In terms of the metrics most critical to understand before executing a promotion, respondents note sell-in volume (66%) and predicted incremental POS (51%) as the top 2 (Figure 8), with the second coming as a surprise to CGT survey partner Capgemini. (See below.) Incremental sales is the top priority to measure after the promotion (also 66%), followed by compliance rates and amounts of cannibalization/halo effect (Figure 9).

Baseline measures took a hit from respondents, only 6% of whom are "highly confident" in their accuracy for reflecting non-promoted volume or "highly satisfied" with the frequency in which they're received.

Attitudes toward performance measurement aren't a whole lot better, with only 9% of respondents professing to measure basic metrics (sell-in volume, revenue, profitability, POS) "very well" and 24% stating that they do the job "poorly" (Figure 10).

With such low opinions of measurement capabilities, it's at least reassuring that most companies feel their promotions typically achieve the planned objectives — if one's standards aren't terribly high: only 27% of respondents say they hit their goals with better than 75% of promotions; 27% claim to still be in the dark about results (Figure 11).

Of course, it goes without saying that addressing all these issues is critically important for CPGs, since the practice is expected to remain just as vital to success in the future: 49% of respondents expect trade spending to increase over the next five years, and only 17% believe allocations will decline (see "The Future of Funding," above).

The more things change in the consumer goods industry, the more they may very well stay the same.

Data and Analytics
Only 14% of survey respondents say that data and insights are the most important single area of focus, which is not consistent with the responses provided for all of the survey's other related questions (Figure 1).

Given the expanding number of initiatives around big data and the growing use of advanced artificial intelligence and machine learning technology in trade promotion, demand planning, and consumer marketing, we are able to determine that data and insights clearly play a critical role.

There is a great deal of work to be done in the area of executive satisfaction with current analytics tools and results, however. No respondents say they were completely satisfied with the quality of performance analytics to which they have daily access; 63% professed to be "somewhat satisfied" and 37% said they were unsatisfied (see "Satisfaction with Real-Time Analytics," above). This is a trend that must change, especially considering the staggering amount of money and resources being invested in big data and analytics tools. 

The survey showed that the most critical data for trade promotion is historical performance results (Figure 2). Evaluating responses from just marketers (see Survey Demographics), we find that 25% feel consumer demand and shopper intelligence is most important. The trend toward greater integration between trade promotion and corporate direct-to-consumer marketing is gaining strength. We therefore expect this figure to climb over the next few years as companies begin combining and aligning trade promotion with corporate marketing planning.

The typical cadence for trade promotion performance metrics has been on a weekly basis. However, the marketplace is beginning to demand higher frequency of performance intelligence, and this was clearly reflected in the 71% of respondents favoring daily reporting (Figure 6).

The benefits to both CPGs and retailers are numerous, including better tracking of market basket/pantry loading and in-store shopping patterns. The support for daily data analysis also includes the ability to better track response to consumer-direct marketing campaigns and to facilitate more timely responses to social sentiment (Figure 6).

Past survey data has found that OOS conditions can be the cause of failure to achieve positive ROI in as many as two-thirds of poorly performing campaigns. That trend may be improving, with 57% of respondents here saying that OOS conditions occur in less than one-third of their promotions. But almost one-third did not know if OOS conditions impacted promotions (see "Out of Stock Frequency" and "Out of Stock Causes," above).

POS data contains far more information than most CPG companies receive, especially if they buy it from syndicated research providers. Twenty percent of respondents say retailers need to do a better job with on-shelf inventory management (Figure 7). Direct POS acquisition could help that visibility.

A considerable amount of money and focus has been devoted to identifying and rectifying OOS conditions, especially from the supply chain, transportation and logistics lines of business. Yet we continue to have this problem. Even when daily inventory data is available, most trade promotion and supply chain systems fail to provide sufficient alerts to take appropriate actions. Respondents were in strong agreement that being able to track inventory daily could potentially end out-of-stock conditions at the shelf.

Of course, daily tracking is only beneficial if the appropriate actions can be taken when alerts are sounded that low inventory conditions exist — either in the supply chain, warehouses or store stock rooms. In fact, the survey indicates that the solution most often requires action on the store floor: poor stocking practices by the retailer and failure to adequately audit conditions by retail execution merchandisers. This is a growing issue among Capgemini's clients, and one that seems to be a growing focus among retail and CPG executives.

Most CPGs will admit to a need to increase the effectiveness of their trade promotion analytics, and nearly one-third of the executives polled feel their analytics are either poor or non-existent (Figure 10). Almost two-thirds believe that their analytics tools and performance measurement are at least satisfactory, with only 9% saying they feel their company does very well (as indicated by their ability to measure multiple metrics on promotions).

Regarding pre- and post-promotion analyses — specifically, which metrics are most commonly measured and reported — the survey asked respondents to identify their top two metrics (Figures 8 and 9).

For pre-promotion, we expected the top two selections to be sell-in volume and revenue. Not surprisingly, sell-in volume was the top metric. Most sales reps will agree that it's their number one objective, and compensation programs clearly reflect that. The same thought applied to sell-in revenue. However, it was not the second highest metric.

What is surprising was the selection of "predicted incremental POS" as the second highest (Figure 8). More CPGs are adding POS to the compensation matrix for sales reps. This is an excellent sign that more serious attention is being paid to the ultimate results of the promotion at the cash register, moving away from the historical practice of pushing product volume into the channel without regard to the end sale.

Additional evidence of this trend is that "incremental POS sales" was cited as the most important metric for post-promotion performance analysis (Figure 9). This indicates that trade planners are paying more attention to the end goal of reaching consumers and driving them into stores. Cannibalization/halo effect and in-store execution compliance were both also high; however, as companies expand and acquire, promotions that avoid cannibalizing other products will become tougher to plan and execute without higher quality data.

Retail profitability was not a key metric, but the future of collaborative trade promotion planning will depend on increased attention and focus for trade promotion systems in the future. We see a higher level of promotion success when the analytics enable a sales rep or account manager to prove reliable retail profitability during planning efforts.

Assessing Baseline Management
Baseline management is beginning to play a far more critical role in promotion planning than ever before. Development and maintenance has traditionally been the responsibility of demand planning, where baselines are calculated and passed to the promotion planners who determine incremental volumes to be achieved.

Only 17% of respondents believe that baseline accuracy is a reliable metric for post-promotion measurement (Figure 9). We continue to see evidence that baseline updates are not occurring frequently enough, causing inaccurate values and calculations as the basis for determining the incremental targets required for promotion planning.

Optimizing the Planning Process
Nearly all respondents report having some form of optimization tool to assist in trade planning (Figure 3). This indicates the critical importance of precision and trust in predicting promotion ROI. But very few respondents claim to enjoy highly sophisticated TPO solutions that leverage advanced technology in AI, machine learning or other forms of cognitive computing.

The adage, “Garbage in, garbage out” is highly applicable to promotion optimization. For starters, we know that on average, as much as one-third of promotions fail to produce positive ROI. OOS conditions are to blame for a significant portion of those failures.

But it’s important to consider the source of the data used to feed the optimization engine. Historically, it has originated from one of two sources: historical results and/or the combination of syndicated and POS data. To confirm this, we asked survey respondents to pick the top two data sources used for promotion optimization, scenario modeling and projected ROI of new promotions (Figure 4).

POS and historical performance data were both selected most often. That's not unexpected, because those two sources are the most commonly available, especially for large retailers such as Walmart, Target and Kroger. Confirming our suspicions, corporate direct-to-consumer marketing promotions and events data were selected by nearly one-third of respondents, indicating the increasing role this data plays in aligning trade with other activity.

Sell-in data (past volume and revenues generated from promotions) is usually considered part of the historical, but we chose to separate it because many CPGs don't have strong, detailed performance data (like tactics, timing, products/promoted groups, individual costs, settlement amounts). A healthy portion of respondents listed this as a major data source for promotion optimization. The lesson here is to ensure that sufficient data sources exist to provide a broad and accurate sampling of data to fuel effective predictive analysis.

Elsewhere, many respondents indicate that digital CP results (mobile, SMS, online) are the most important data additions to TPO (Figure 13). Advertising continues to play a huge role in consumer engagement for CPGs, and aligning those schedules and event calendars with the trade calendar is a critical requirement for improving performance and avoiding channel conflict. Social sentiment also is a powerful tool, used in a real-time scenario to gauge the consumer environment and either leverage a positive sentiment about a product or company or avoid spending money in a negative environment.

Specific to impacting ROI, more than half of respondents say consumer promotion data will marginally increase ROI, but another quarter believe it will drive a dramatic increase (Figure 12).

On that topic, most respondents feel that their trade promotion activity is at least "sometimes" aligned (Figure 5). This is surprising, since we continually see traditional information and communication barriers between marketing and sales inhibiting effective integration into the trade planning process.

What we are seeing is an encouraging trend among TPx vendors to enable some degree of integration between the marketing and trade calendars.

Making Progress
About 25% of respondents say their promotions meet objectives more than three-fourths of the time. Sadly, the same percentage don't even know how their promotions do, as they have no visibility into performance (Figure 11). More open collaboration with retailers is key to fixing this.

Overall, the survey illustrates considerable progress being made toward more effective trade promotion management and execution. With the advent of increasingly sophisticated technology and tools, today’s CPGs are able to collect and synthesize critical data sets, revealing insights and actionable information never before possible. They can overcome the historical challenges inherent in measuring effectiveness and embark on a new future where the significant investments in trade promotion can be effectively measured and managed — and ultimately will translate into a positive impact on the company’s bottom line.

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