Marketers in CPG industry are constantly faced with the challenge of allocating their fixed marketing budget among various marketing channels like traditional communication mediums and digital channels.
To achieve effective budget allocation for marketing, companies have to rely on many of the marketing tools available today and create models that will show the impact each of this channel has on sales.
This case study will summarize the various marketing mix models available for marketers by analyzing the strengths and weaknesses of each of them. In particular, we focus on regression models, influence maximization models; agent based models and empirical methods being used by the marketers.
Markdowns are omnipresent in retailing and play a significant role for any retailer’s strategic decision making.The need for markdowns arises because of variations between predicted sales and actual demand in the selling season.
Despite the benefits it offers, many retailers find it difficult to implement the tool because of the complexities involved and adverse consequences that result because of sub-optimal markdowns. The challenges most retailers face today are threefold i.e. determining optimum markdown levels, determining timing of markdowns and identifying goods for markdown.
In this paper, Perceptive presents a methodology that enables a retailer to track performance of its merchandise and identify items that are under or over-performing and also discusses various best practices using which you can maximize value from markdown optimization.
The client has started a closed-ended hedge fund of $15 MM. With three types of investments available, each with a different rate of return and associated risk, the client wanted to analyze the returns under various scenarios.
Faced with this challenge, the client approached Perceptive Analytics to build a model that tracks the performance of the fund and recommends action to obtain maximum return for the selected scenario.
Our model helped the client as a decision support tool for investments in various instruments. The client was also able to optimize the risk by balancing the funds amongst various investments.
The client decided to spin off its gas division and focus independently on it. The newly formed entity provides two types of services viz., gas distribution and gas filtration.A major challenge in gas distribution business is fixing the tariff rate.
Faced with this challenge, the client approached Perceptive Analytics to build a model that could fix the tariff rate to be given to its end users.
We helped client assess the financial viability of the spin-off decision. The model presented a comprehensive control of key variables affecting the tariff rate. It calculated key metrics like ROI and IRR for a given tariff rate and also vice-versa enabling the client to set a competitive per gallon price.
The client, a non-profit art organization as part of its outreach programme saw strong growth in student enrolment and the number of artists participating in monthly exhibitions. To meet growing demand, the organization decided to renovate its premises and add additional space.
The renovation of the building is a costly affair and the management wanted to make sure it had sufficient cash flows to mitigate the risk of stalling work. Perceptive Analytics built a revenue model that simulated cash flows under various scenarios. The model highlighted cash crunch situations under each scenario allowing adequate time to take corrective measures.
Coupons are recognized to be a highly effective sales tool for all businesses. In 2012, marketers distributed around 310 billion valued coupons at $484 billion. Today coupons affect commerce in much more powerful way than ever before and coupon deals have become a cutting edge promotion tool. Marketers view coupons as an efficient way to influence the smart newer generation of busy consumers. But, if the managers are not careful with the design and timing of the coupon campaign, coupons can have negative impact on business performance. We identify face value, expiration date, purchase requirement, timing and distribution of coupon as the most critical factors in optimizing a coupon campaign.
The client is a financial services company based in New Jersey, USA. It focuses on financial planning, retirement solutions, and insurance. It has a dedicated loyal customer base and is essentially an offline company depending largely on sales people for lead generation. The customer base of the client is geographically bound to the surroundings of New Jersey and Maryland.
The client started its online presence in the form of a website 10 years ago but hardly ever realized any business from it. It has also invested considerably on the web, in the form of advertisements and content, in an attempt to attract new customers. However, the investment has not produced any decent results. The problems identified include: Low relevant traffic, inappropriate landing pages, high bounce rate, low engagement and poor conversion ratio.
PayAvenue is a payment gateway provider that allows users to pay for their online purchases. Its services are accepted by a wide range of both retail and non-retail merchants across United States. For every online transaction, PayAvenue charges a small amount as commission. Utilizing transaction data, PayAvenue can enter into strategic partnerships with its clients.
The CEO of PayAvenue, realized that the current business had low margins and he wanted to move to a higher margin alternative with the existing operational setup. With this objective, we analyzed the data to find significant cross-selling options.
NiteFoodie is a medium sized American fast food chain based on the west coast. The client offers a variety of foods and serves around 20 million consumers each year through its 60 restaurants.
The company had entered food truck business two years ago and expanded aggressively to gain market share. Due to the fast paced growth processes in the company did not evolve for large scale services. The company faced low margins in its packaged food distribution and found that there was scope for significant improvement.
The company needed a solution to optimize food pack distribution to reduce costs. Perceptive Analytics analyzed processes and developed a tool that optimally distributed the food packages required among the carriers. The whole exercise resulted in annual savings of nearly 17%.
This report draws attention to the need to recalibrate bank branch networks as the role of branches is changing from being a major transactional channel to a point of interaction. We analyzed existing branch locations of 8 national banks: Citibank, Chase Bank, Bank of America, US Bank, Wells Fargo, TD Bank, Capital One and Fifth Third Bank in major cities such as Chicago, New York, Dallas, Los Angeles, San Francisco, and Boston.
To evaluate branch location strategy of banks, we collected data on market shares of these banks in respective cities. Branch-wise location information in each of these cities was also obtained from the Federal Deposit Insurance Corporation (FDIC)’s Summary of Deposits (SOD) database. We collected data on demographic variables such as neighborhood information, number of households, and mean income per household for all the cities.
Using the market share data and p-median model we suggested the number of branches and branch locations that a bank should have in a particular city. Then we compared our proposed network with the existing branch network. Findings suggest that some of the banks need drastic changes in their branch locations, for example Citibank in San Francisco has scope to increase the number of branches by 30%. While TD Bank is over represented in New York and can reduce its branches by 33%. These changes will help these banks to improve profitability in the long-run.
This report draws attention to the poor performance of Indian economy during the last few years. To study the effectiveness of India’s economic policy, we sampled relevant macroeconomic variables for India over the last 30 years. Data reflected anti-growth conditions. The average bank lending rate during the period of our study has been 14.32% and inflation has been consistently around 8%. Income disparity in India increased over the studied period. According to World Bank data in 2009 India ranked 78th in the list of 134 countries.
We compared India’s monetary policy with other high GDP economies such as USA, UK, Italy, Switzerland and developing economies like China and Brazil. Most of the developed countries managed to maintain low-interest–low-inflation combination for most of the years following 1990.
We suggest that high interest rate policy is incorrect for India. Low interest rates have a stimulating effect on the economy because they make it more attractive to borrow and invest. Increased investments in profitable ventures will lead to increase in output and employment. Therefore lower interest rates can help achieve faster sustainable-inclusive growth.
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