On 7th September, our group had the privilege of conducting a Harvard Simulation on Hartnow Technologies, which is a 5-year old company that specializes in 3D printers. The main goal of this simulation involved maximizing the company’s market valuation, through controlling the distribution of marketing budget across areas of branding, marketing and sales, customer service, loyalty/referral programs and strategic development. Overall, this simulation enriched my knowledge of ‘Strategic-Marketing Management’ as it allowed me to gain insight into its complexity and what it means for a company to be ‘consumer-centric’ from a practical standpoint.
Initially, our team did not have a specific strategy as majority of our decisions were reactive in its approach. By this, we based our decisions with the intention towards generating the greatest volume of customers across all three sectors. Essentially, this reflected our team’s initial strategy of ‘acquisition’ in which our decisions were conducted on an experimental-based approach. This area of experimentation similarly applied within our purchasing of reports like: number of customers gained/lost by sector and lost revenue by sector, which provided us information on which sectors were most profitable. Overall, our whole team seemed to be congruent with this approach as it allowed us to find out the impact of our decisions from the previous period. Moreover, this would allow the team to make better decisions within the future periods as it would give us informed results of which sectors were of most value to the company.
As for myself, my understanding was to look at the simulation within its entirety and base our decisions on the dynamic-segmentation approach, also known as the Acquisition-Expansion-Retention model. After numerous discussions, our team decided to implement this model as our overall strategy from period 5 onwards. This gave us clarity in our marketing decisions as we knew exactly where we wanted to allocate the budget and how we wanted to allocate it based on applying a heuristics approach. This change in strategy shifted our mindset from reactive to proactive, where we bought reports on what we were lacking from the 4 Marketing Principles. Furthermore, majority of our decisions not only considered the immediate effects it had on the following period, but also the long-term impact and how it will be of use within future periods.
If give the opportunity to play again, I would have continued to incrementally increase budget allocation in branding throughout the whole duration of the simulation. As a team, we were in the stage of ‘retention’, in which we shifted majority of our investment in branding towards areas of the loyalty program and customer service. Throughout the simulation, we were all under the assumption of branding to only have effect in areas surrounding the number of new customers gained, which we weren’t focusing on at the time. However, I later understood branding and its ability to address both acquisition and retention stages, as it allows companies to target the “right customers”, in which majority are markedly loyal over the lifetime of their relationship with the brand (Lischer 2018).
Lastly, I would have made a more conservative split to the budget allocation throughout the whole course of the simulation. During the second half of the simulation, our team implemented a ‘heuristics-based approach’ where we split the budget according to the size of sectors by percentage. This was implemented once we purchased the sector-specific CRM package. However, once we used this metric we ran into the trouble of having a volatile difference in an unequal distribution within our budget allocation. This was evidenced in Period 8 when we allocated $1,301,883 in the loyalty program for the automotive industry and a mere $39,742 for external sales within the aerospace industry. This decision proved to be costly as directly afterwards in Period 9, our market valuation plummeted from $140.2 million to $88 million. Although we achieved our goal in retaining a steady number of customers from 604 to 780, it was our customer growth rate that had the greatest impact in which it went down from 23.5% to -25.5%. For next time, I would make sure to split the budget with a reasonable judgment that takes into consideration both the effects of acquisition and retention.
Having conducted this simulation once, the key learning that I developed has been understanding the overall complexity surrounding strategic decision-making. Before taking this course, I often heard so many companies claiming to be ‘customer-centric’ but was confused as to what this exactly meant and what this process looked like when it came to formulating budget decisions. This involves going beyond the false assumption of decisions having a linear approach, but considering its multiplicity and its influence within a variety of areas including: trade-offs, revenue enhancement and cost reduction. Essentially, it made me realize that achieving all these four objectives are both conflicting, yet collaborative at the same time. Within our team’s simulation, having this type of understanding would have ensured our team to make more informed decisions that took into consideration the respective trade-offs across all different areas of the business.
Additionally, we made another mistake within our simulation of directing majority of the budget towards the biggest customer segment, the automotive industry. However, we soon realized that this wasn’t the right approach as ‘customer centricity’ is not about neglecting other customers but rather building a growth strategy around the customers that provide you the greatest value. From this, we learned that CLV is a better measure than market size when making AER decisions, in which it captures the true contribution of each customer by accounting for customer heterogeneity and dynamic effects noticed at the individual level.
Understanding that all customers differ was applied when we bought the Detailed-Customer-Reporting Module, which allowed us to segment via different sectors based on the annual customer data at the individual customer level. This was important as customers and their inherent value differ across sectors and thus have varying needs and behaviours which require strategic allocation of resources. Additionally, this available information allowed our team to formulate budgeting decisions that were customer-centric, in which it gave us an understanding of how our customers were different. By this, we were then able to identify which customers were of most value to us, in which we eventually focused on increasing investment within defense due to it being the largest in CLV.
Overall our team managed to take into consideration the changing nature of customers by adopting the AER model, which was utilised throughout the simulation. Towards the end, our team adopted the CLV approach where we divided the budget according to the percentage of CLV across the three sectors. Despite its small market size, we identified defense as the sector with the highest CLV in which we allocated majority of the budget within this area as a way of supporting the customer-centric culture.
Within our simulation, we didn’t place enough emphasis on this specific marketing principle, which was reflected in our inability to develop a sustainable competitive advantage. This was evidenced in our lack of investment towards the BOR-equity-stack, where we didn’t consider the nature of external threat amongst outside competitors within the later periods. This is shown with our low investment of $310,000 in branding and a massive $1,852,406 simply on customer service in Period 9. As mentioned in Seminar 8, branding and relationship management is a long process that involves consumers to think, feel and act. If we continued to take into account these 3 elements within our decisions, this would have ensured Hartnow Technologies to gradually position itself as an industry leader, with the ability to command at a premium price.
Our team properly applied this marketing principle from period 6 onwards when our budgeting decisions shifted from being experimental-based to an adjusting-heuristics-approach. The budget was split based on the percentage of market size across the three sectors, where it allowed us to use a specific metric for what we wanted to control. Despite its effectiveness, it would have been better if the team utilized other methods like the response model or log-log model, which would allow the company to leverage the data from the past to isolate the relationship between marketing resources and performance.
In conclusion, the Harvard simulation proved to be an insightful experience and has allowed me to understand the significance of tackling a variety of issues surrounding customer centricity. These include the importance of monitoring a company’s performance, the challenges associated with the optimal resource allocation in response to changing consumer preferences, customer relationship management and responding to the threat of competition. Overall, my understanding of ‘customer centricity’ has evolved from viewing it as a mere concept into treating it as a mission that requires action evidenced within the clarification of its defined metrics and established goals.
Lischer, B 2018, Why Invest In Branding? 5 Reasons You’d Be Crazy Not To, Ignyte, accessed 28 October 2018, < http://www.ignytebrands.com/why-invest-in-branding-5-reasons-you-would-be-crazy-not-to/>.
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