Challenge

In the era of digitalization, one might wonder if promotional activities based on printed leaflets possess the impact they had in the past or whether there are customers more prone to be responsive to this type of engagement.

Accordingly, our client, a retail company, was seeking help to redefine its promotional leaflet distribution strategy.

The main goals were to increase store traffic and conversion rates while maintaining the current budget spending which limits both the leaflet number and distribution zones.

Solution

The first step was to understand, at the customer level, which segments reacted best to the printed leaflet distribution through pilot testing. To assess the impact of the distribution strategy, a control group, who did not receive a leaflet was established in every distribution cycle. Then, our predictive model benchmarks people with similar characteristics from the test and control groups to quantify which are the customers more sensitive this type of contact.

The impact estimations were incorporated in an optimization model to determine the ideal zones and leaflet number to be distributed.

Results

By evolving decision-making from a purely empirical and static standpoint to a data-driven process, our client can quantify and understand the effects of targeted promotional activity on different receivers.

The project reaped benefits by maximizing positive customer engagement with the leaflet leading to increased sales and optimized advertisement costs, while minimizing unnecessary resource usage. Keeping the same budget, we were able to obtain an additional return of 14 cents for each euro invested. The implemented model was internalized at the client, enabling autonomy from the team and allowing the creation of long-term value.

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Insights

  Increasing loyalty by executing customer retention and acquisition promotional campaigns has been one of the main marketing objectives for the last decades. While marketing and commercial teams work hard to deliver the best value to customers, the brand-customer relationship is becoming more dynamic and multi-layered with customers having increased competitor awareness and preferring to use proximity stores to cherry-pick shopping baskets.   Whether on retail, banking or any service companies, the strategical planning of loyalty targeting and promotional activity is becoming a very complex task which, if not built upon solid profitability evidence, quickly turns into a  resource-usage and margin-diminisher managerial problem. While the promotional strategy is typically enforced on a top-down matter, the customer response complexity and dynamicity urge a bottom-up approach that manages to ensure one-to-one ROI focused on real customer needs and basket growth opportunities. A broad analytical vision of the customer behavior history and future response predictions are the most valuable assets towards ensuring the promotional value proposition is suited to customers’ needs and ready to drive long-term profitability.   Embracing the dynamic customer’s lifecycle A typical promotional strategy is usually defined over some sort of low-dimension customer lifecycle segmentation, more often than not based on the core RFM variables (Recency, Frequency, and Monetary value). However, both customers and companies are engaging in a multitude of ways, in which different products and channels are tailored to target specific customer needs, such as convenience, large-basket destination stores and online retailing. In this era, the increasing customer price awareness along with the emerging promotional make loyalty a much more dynamic and hard to acquire concept. While RFM models have very valuable predictive power and provide a simple but effective way of segmenting customers, they fail to pick up on the necessary consumer behavior detail to fully optimize the marketing relationship. One could dive even deeper and find such different behaviors within the same customer, perhaps across different product categories. Multi-product companies typically face very different competitors across their assortment range, and customer retention and churn measurements should also include this diversity dimension. Otherwise, the company may never extract the full potential from its customers and may be losing more money than they even realize. Broad lifecycle segmentation is no longer enough to respond to the evolution of customer loyalty, as companies need to figure out the driving needs for customer shopping and understand their true potential value and, ultimately, profitability. The pursuit of one-to-one ROI Figuring out the customer level ROI is all about the understanding potential for customer improvement and the relationship between customer investment and expected probability of growth activation. Analyzing customer potential is two-fold. It involves computing detailed customer-need specific lifecycle segmentations and also mining association rules to estimate them for customers with insufficient transactional data. Customer level information is obviously extremely rich on providing information for this value. The next step is to get answers on how to acquire that value potential. Past promotional campaigns and extremely frequent A/B testing should be deployed to accurately analyze the efficiency of each promotional vehicle, communication channel and promoted products on converting customers’ potential value into loyal recurrent transactions. Developing these predictive models enables companies to avoid the most common mistakes of promotional planning: Focusing on quantity instead of quality: Having too many promotions will, at most, maintain customer’s cost perception while eroding most chances of sustainable profitability; Ignoring customer data on the promotional definition: Suppliers are the main source of promotional funding, but the value of such mass promotions are limited regarding the impact of customer growth and acquisition. When strategic guidelines are spread down from general segmentations, customers may end up getting targeted for promotions they don’t need, on channels they don’t respond to, and with a disproportionate value regarding their potential brand engagement. The only way to avoid this issue is to strategize not on the promotion and channel usage, but on how different customers should be targeted. Before deciding on what to offer to each customer, companies should strategically define: Which segments to invest and disinvest in? What is the target ROI to decide on whether customers should be contacted? What impact will the available campaigns have on customers’ shopping patterns? How much will each customer segment, if targeted, increase market share or profitability?   Then, by compiling all the type of discounts and promotions that may be included in campaigns, companies should be able to look at the expected response for each pair of customer-promotion and figure out the expected customer response in shopping behavior.   Overcoming organizational challenges Effectively planning promotional activity across the company, making sure both customer needs and supplier funding opportunities are being considered, poses three main challenges to large companies: Managing and navigation huge amounts of data; Building a robust promotional planning process that brings all stakeholders’ needs and capabilities on board; Ensuring the customer-level promotional portfolio is correctly planned instead of having promotional silos that may have cross-promotional effects and thus erode profitability.  There is no point in devising the ultimate promotional campaign for the customers if the commercial teams can’t fund it and there is also no point in engaging in non-profitable promotional activities that contribute little towards achieving the targeted customer value proposition. Overcoming these difficulties requires developing all the processes and frameworks that enable all stakeholders to work together during promotional planning stages. Having the ability to plan, control and evaluate mass and customer-specific promotional activities is the only way to ensure marketing investments have sufficient returns and to avoid them turning into a margin-consuming spiral activity. Only then can companies pursue purposeful customer marketing strategies and develop promotional proposals that leverage customer knowledge into sustainable growth.

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The combination of an increasing relevance of the online channel and the power of advanced analytics has brought new opportunities for COOs (Chief Operating Officers) to position their departments as value centers. In this context, the concept of ‘value of service’, e.g., understanding the net impact of providing same day deliveries, emerges as a powerful tool to improve the clarity of discussions in organizations and to bridge the gap between marketing and operations. This paradigm contrasts with the traditional positioning of having operations merely satisfying marketing requirements at the lowest cost.  For years in the offline channel, the dialogue between marketing and operations was often dominated by the trade-off between in-stock rates and inventory turnovers. In the online channel, the natural extension of this organizational posture was to add speed as another performance indicator. This reactive stance hindered the fact that operations are even more central in this online world and that more value drivers (such as quality of packaging, preciseness of delivery, and flexibility of delivery slots) become as important to factor in the aforementioned dialogue. More interestingly, the rising power of advanced analytics makes it now possible to anticipate the quantitative relation between value drivers, customer lifecycle value and supply chain costs — ‘value of service’, avoiding misunderstandings and therefore, providing more alignment within the organization. Customer expectations in the online setting have made it hard for companies to find a way to break-even. By acknowledging the ‘value of service’, it is possible to increase the pace and efficacy of testing new service models that may result in a future competitive advantage. Moreover, better service trade-offs can be attained by discussing simultaneous levers, such as supply chain costs and delivery lead-time. To apply this idea, cross disciplinary teams involving operations, marketing, and analytics members must be set up to jointly determine the value drivers to analyze, the success metrics to use and the most suitable methodologies to apply.   We have performed extensive consulting and research with online players and other companies in different retail sectors. In a particular case, after performing several econometric analyses, we ended up justifying the opening of several distribution centers, based on the increased revenues they would generate through better packaging, delivery preciseness, and improved lead times. The work of Marshall Fischer and others (Marshall L. Fisher, Santiago Gallino, Jiaqi Xu, 'The Value of Rapid Delivery in Omnichannel Retail') was a stepping stone in terms of methodology and scope to pursue a central idea — there is a hidden opportunity of improving e-commerce businesses when understanding operations as a top-line enabler. Written by Pedro Amorim (LTPlabs’ Co-Founder & Professor at Faculty of Engineering of the University of Porto) & Paulo Sousa (Manager at LTPlabs).

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The corporate world has been embracing analytics and artificial intelligence systems within an increasing number of business functions that have traditionally been oblivious to its potential. Additionally, the digitalization of the customer journey is increasing the amount and detail of data that can be accessed, which provides tremendous value towards decision-making and personalization efforts when properly addressed. These new possibilities pose several challenges throughout the marketing mix that require an ever-increasing pool of analytical solutions. Therefore, companies that do not adopt these techniques within their processes will soon become outdated and lose market competitiveness. In addition, the deployment of analytical tools to the different marketing functions will increase marketers’ decision-making quality by empowering them to deliver the right product at the right price and at the right place to the right people. Now, which marketing activities have the largest upside regarding the deployment of analytical methodologies? We will explore this question by enumerating several applications that companies should be looking to improve soon and by examining a recent client analytical transformation. Still, figuring out areas of improvement is not enough for marketing departments to embrace the future, as they must first adapt their own skills and capabilities to embrace a new age of Marketing. Leveraging the P's of analytics Recently, marketing departments have started to deploy analytical processes and artificial intelligence into the marketing mix of their brands’ portfolio, but the full potential is still left uncovered. Product optimization decisions will increasingly make use of detailed consumer market data and forecasting models, whether deciding a retailer’s assortment or a Consumer Packaged Goods (CPG) company’s portfolio. Additionally, marketers planning distribution channels will find tremendous opportunities when challenged with sales location planning or customer contact optimization. Promotional planning, recommendation systems, product pricing and media planning are all activities that will further require personalization efforts to maximize their efficiency. And finally, the customer of the future will be increasingly demanding towards brands, turning personalized and efficient communication from a competitive advantage into a must-have for any corporation. While there is extensive potential for analytical processes, companies should realize that the combination of analytics with manager experience and creativity is still paramount for the success of improvement initiatives. Moreover, resource investment should always be as effective as possible, so expected returns on investment (ROI) should always be evaluated before jumping into any transformational endeavor. Artificial intelligence supporting marketing decisions Take the work we have recently done with a large retailer, in which there was a concrete opportunity to leverage both the teams’ business knowledge and the overwhelming richness of big internal and external data to improve product assortment decisions. The teams clearly knew that analytic processes would be extremely helpful not only to identify and forecast overall market trends, but also to support the customization of product selection to store-specific customer characteristics. In the past, mass customization across all the stores was too complex to be included in the decision-making process, but specially designed analytical models and a careful parametrization of category strategy enable the fine-tuning of managers’ assortment decisions to maximize profitability and to improve supplier negotiation efforts. The new age of the marketing function The megatrend of digitalization has had a corporate-wide reach across all business functions. Marketing departments have typically had a hard time embracing all the opportunities that come along, mainly due to a misalignment between the required capabilities and the typical marketer skills. Typically, companies have either outsourced or leveraged expert internal teams from other departments to obtain the required capabilities to embrace artificial intelligence in their marketing processes. However, with analytics becoming a reality in marketing departments all the way from strategy to operations, this procedure is neither sustainable nor capable of promoting process adjustments and continuous improvement. As such, not only should CMOs promote the integration of analytic-savvy teams within their departments, but marketing business schools should also include these skills within their curriculum. The marketer of the future will combine creative and business skills with analytic and digital capabilities to embrace the incoming challenges, and the first-moving organizations into this field will gain a competitive advantage for the new age of marketing. Written by Pedro Campelo (Senior Consultant at LTPlabs and Professor at IPAM) & Liliana Silva (Junior Business Analyst at LTPlabs).

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To have impact, numbers need stories and vice versa. For business to form a complete picture, they need both Big Data (quantitative information) and Thick Data (qualitative information). Each of them produces different types of insights at varying scales and depths. They may yield interesting synergies and complement each other.   In particular, the tactical decision-making level has a large decision-making gap that can leverage this ‘dual’ approach. On the one hand, analytics-based methods are able to quantify and generalize insights (Big Data); on the other hand, there are empirical approaches that can improve the business perspective and the depth of analysis (Thick data). We’ve devised four main methods to integrate Thick into Big Data: Raise hypothesis: use Thick Data as a source of inspiration to raise hypothesis that are then tested over the population with Big Data Confirm correlations: check Thick Data insights to confirm correlations that were found through exploratory Big Data analysis Warm-up: plug Thick Data numbers into the warm-up phase of a Big Data project Full connector: start using Thick Data similarly to the Warm-up method, but then continue to use it to keep calibrating the Big Data model   Big & Thick Data in the Telco industry with a full connector approach A great example of a blend of Big & Thick Data is a program launched by a leading Telco provider, with the goal of empowering staff to interact with customers on a personalized level. The first approach was to combine 79k data variables around customer service and marketing into a single Net Promotor Score (NPS). This model helped move from around 30k NPS attributions per year to scoring the whole customer base with an accuracy of around 80%. The next step was a sentiment analysis over call center records to move into contextual customer insights, thus refining the NPS score. Leverage NPS data allowed to differentiate marketing strategy and interaction guidelines between customer groups (detractor vs. passives vs. advocates). For example, to make carefully crafted outbound calls for detractors and use electronic direct marketing (EDM) for advocates. The insights obtained were also essential to ensure there was a clear follow-up point to use with each customer. To engage staff was built a simple internal portal, with NPS, interaction scripts and key client info. At this point was critical to create strong engagement links with IT. During the first six months, 50k customer issues were identified and solved. As a result, the customer perception (NPS scores) improved.   The path towards transformational projects As we can learn from the previous case study, transformational projects involve cross-functional skills in Thick Data, Data Science and Management. Effective transformational projects that leverage Big and Thick Data require: a holistic perspective of the problem, covering multiple integrated processes, stakeholders and KPIs a solid methodological approach, to grasp the synergies of both data streams  a sustainable cultural change towards the execution and adoption of such projects to support decision making  

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