Table of Contents
- Price for a
- Anticipating customer needs with predictive analytics
- Optimizing campaigns with Predictive Marketing
- Building and using predictive models
- The conventional campaign approach
- The Predictive Marketing approach
- Select the right customer
- Select the right channel
- Select the right time
- Select the right offer
- Increasing marketing productivity
- Optimizing interactions across all channels
- Related Free Business Essays
Metlife has been providing insurance for over 100 year and is a leading global provider in the insurance, annuities as well as benefit program that serves 90 million customers. As such, through its affiliates, as well as, subsidiaries, holding the leading market positions worldwide. The companies offer annuities, home insurance, life insurance and monetary services to individual as well as group insurance and retirement and saving products presented globally, through agents, third party distributors like banks and brokers as well as direct marketing channels.
Arguably, Metlife is currently trading at an approximate 45% discount from its book value of equity. As such, it results to the lagged cost of equity (COE). Tentatively, ROE has been 91%, whereas COE has been 10 % to 13%. Nonetheless, the ROE has approximately been 4 %, thus impacting the stock prices. As such, the high end of the price presents the estimated value of the firms’ effort in augmenting or improving the ROE, from the predominant level. Nonetheless, the higher scenario of probability is that ROE will remain at or below the current level that will make the high end price valuation unachievable.
Additionally, historical as well as historical review and peer multiples in trading in the lower multiple in comparison to the competitors; nonetheless, a majority of US based life insurer are presently trading at varying discounts. As a result, a weighted multiple for each segment presenting that Metlife may be trading near a fair value price presently, nonetheless, in contrast to the company’s historical multiple, they trade below the five year averages in both price over a book as well as price over sales.
The overall market insurance and specifically in England, is a mature and established market with large well capitalized firms. As such, there exist little differentiation in the products presenting and competition bases upon product reduction pricing in drawing the customers. As such, a mature market such as the England market could realize a reduction in the total revenue, thus it could be detrimental to the long term profitability of Metlife. Therefore, factoring in investment risk further augment the reliance on the revenue growth as well as consistent investment returns, that cannot be guaranteed.
Tentatively, Metlife’s performance in its investment portfolio plays a fundamental role in the profitability and sustainability of the company. Each annuity policy exposes Metlife to a future financial obligation. Thus, it is fundamental that sufficient investment gains be maintained in ensuring its capability to meet the required payments. As such, the employment of reduction in annuity sales will curb the presumably increased liability. Consequently, this action presents the critical investment nature returns and their impact on Metlife. The main holdings are investment grade cooperate and governments form England and selected foreign countries. In the past five years, the company had an investment portfolio growth of 5.5% return, nonetheless, the last year returns was 4.8%. This presented a potential for financial issues should overall rates of interest remain short. Additionally, an exposure to certain volatile foreign markets both in their operations as well as fixed income investments would be proficient in augmenting the revenues.
Consequently, the growing of their international operations through substantial investment in the established, as well as, the developing market. This entrust the improvements I supporting the liability in increasing the firms value in the predominant England market. As such, it will maintain a dominant force in the life insurance market and the overall leader in the market. On the other hand, it will enable the company to successfully weather any financial crisis, thus augmenting its revenue and net growth (Michael, 1990).
Most companies depend on some form of direct marketing to obtain new customers and produce additional revenue from existing customers. The dispute, nonetheless, are numerous. As such, in any given market, hundreds of companies contend for the minds as well as wallets of the same consumer groups. Million-dollar marketing campaigns may fall short in a generation to the response necessary to generate revenue or even cover the cost of the campaign. Arguably, consumers at present have many options, but when they are snowed under with mass marketing in their e-mail inboxes and mailboxes, and through their telephones, they regularly choose to simply ignore the messages. In addition to with the augmenting popularity of personalization and endless segmentation, consumers are conditioned to anticipate more targeted offerings. Consequently this implies that the difficulty of marketing campaigns—and the complexity of reaching receptive consumers—will only continue to augment.
In order to assist in keeping rapidity with today’s consumers, Metlife should implement campaign management systems that facilitate them to produce many more campaigns than was previously possible. Whereas these systems augment the number of campaigns that Metlife can run, they do not unavoidably develop campaign effectiveness. In reality, by producing more campaigns than ever—with no improvement in targeting—campaign management principle that can essentially contribute to the growing endemic of consumer dissatisfaction.
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An outbound campaign, on the other hand, is an effective approach in marketing the company’s output. By shifting from large, distracted campaigns in the direction of highly targeted campaigns that address the requirements of individual customers, as such England MEtlife company would achieve have substantial development in campaign efficiency as well as profitability, and noteworthy increases in exchange rates and customer satisfaction.
By building a campaign strategy in the region of customer requirements, in comparison to simply around internal priorities, companies accomplish the double advantage of augmenting revenue as well as customer satisfaction. Tentatively, rather than choosing the most excellent customers for each campaign, these companies prefer the best campaign for each customer.As such, by gaining an in-depth comprehension of the requirements and preferences of each customer, these companies are able to make much beleaguered offers that have elevated likelihood of reception. Therefore, they achieve this with no increase in their marketing staff or budget.
In addition, Metlife are able to attain these remarkable results using Predictive Marketing, a solution for optimization, campaign creation, and implementation that coalesce predictive analytics technology as well as business. As such, it assists Metlife in determining what their customers and prospects desire, and how to meet the customers’ needs in a way that satisfies customers and engender maximum revenue. As such, if the company is proficient with the knowhow of reaching the right customer with the accurate offer at the right time, through the right channel, Metlife has the key to flourishing unswerving marketing campaigns.
Tentatively, when companies facilitate marketers to optimize campaigns without relying on statisticians to construct models, they advance productivity as well as competence throughout the campaign process.
Anticipating customer needs with predictive analytics
Predictive analytics examines historical and present customer data to generate predictions regarding future performance, preferences, and needs. Analytics into Predictive Marketing is a way that allows marketers to make the most of the potential of the present technology. As such, Predictive Marketing presents marketing departments the logical power that was previously available only to statisticians— without the difficulty. Consequently, by including predictive analytics into their every day campaign processes, and accumulating their business expertise, marketers are able to appreciate and arrive at customers to an unparalleled degree, that would result in more effectual campaigns and noteworthy augments in revenue.
Optimizing campaigns with Predictive Marketing
Arguably, in order to optimize their marketing campaigns, Metlife ought to be able to answer the four decisive questions: when to make the tender who to contact, what to offer, and how to make the offer. Tentatively, Predictive Marketing allows marketers to find the answers speedily, and to produce as well as implement campaigns around this simple but effective process.
To start with, marketing analysts generate predictive replica to professionally find appropriate customers and realize the best channel, timing, and message for their insurance customers. Subsequently, marketers add business information such as budget guidelines, contact restrictions, and campaign strategies. Consequently, prior to sending the campaigns, they appraisal the projected estimate and cost of each operation, in addition to the response and revenue that every campaign expects to produce (Michael, 1990). In conclusion, the marketers execute the approved campaigns. Upon completion of Predictive marketing, they use Predictive Marketing to contrast specific results to the projections, and slot in information that can improve the efficiency of future campaigns.
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As such, this process accomplishes in Predictive Marketing’s two key modules, the Analytic Center and the Interaction Center. As such, the Analytic Center formsthe predictive model that permits Metlife to look forward to the requirements as well as preferences of individual customers. Therefore the Interaction Center function in the creation, optimization, and execution campaigns based on the customer requirements predicted by models created in the Analytic Center. Jointly, the Analytic Center and the Interaction Center facilitate companies to answer the “who, what, when, and how” of winning campaign marketing.
Building and using predictive models
Additionally, Predictive analytics allows Metlife to use their obtainable customer information to model numerous different types of future customer behavior. Nonetheless, Predictive models make marketing campaigns more effectual and gainful by enabling marketers to include only customers that are probable to admit a scrupulous offer or respond to a certain message. Metlife should run an assortment of campaigns in the accomplishment of specific goals that include: acquisition, cross-selling, and retention (Michael, 1990).
Predictive Marketing creates a range of models that include:
An acquisition model predicts how probable it is that a prospect will exchange, or buy a company’s product or service.
Cross-sell models that forecasts the probability that an available customer will buy an supplementary services.
Up-sell models decide the likelihood that an existing customer will make a supplementary investment in a product or service, for instance upgrading to a more luxurious wireless telephone strategy.
Attrition models that forecast the likelihood that a customer will stop buying or using Metlife’s services.
Value models assist in forecast of a number of dissimilar value measurements, like a customer’s expected lifetime value, or the anticipated production value if the customer buys a definite service.
Tone-of-voice models. Since different messages reverberate with dissimilar customers, Metlife employs tone-of-voice models to forecast which message is most excellent for each customer.
Risk models will facilitate Metlife in the prediction of the probability of deceitful activity or loan defaults. Loan approval as well as Claim departments employ these models, which can as well proficient in excluding high-risk customers from marketing campaigns and to presage branch offices and employees about potential risks.
Metlife typically forms models for each service and channel. Thus, the total number of models used, thus, relies on the number of services and channels. Metlife frequently generates models for new services. As such, as the company has well built models, Predictive Marketing offers the indispensable campaign optimization ability that will allow the company to fully leverage their predictive insights. As such, by slotting in results from existing models, as well as adding optimization capabilities and, new modeling Predictive Marketing will enable Metlife to employ their predictive models and methodologies to better advantage.
The conventional campaign approach
In most cases Metlife uses conventional approach to direct marketing, initiating between 10 and 50 large, calendar-driven campaigns each year. Thus, the marketing department scuttles its life insurance campaign in January, for example therefore this approach principally centers on internal company processes, in comparison to centering on the requirements and preferences of its customers (Michael, 1990)
As part of the traditional campaign advance, marketers characteristically select customers using a few essential selections or exclusions. Therefore, Metlife may prohibit customers who already own product or who are on an opt-out list, or comprise only groups within convinced geographic boundaries. Arguably, the response to these types of conservative campaigns is normally low—often less than one or two percent.
The Predictive Marketing approach
Consequently, the Predictive Marketing’s centers on customer requirements and preferences will allow marketing departments to expand campaigns that create elevated response rates. Thus, by choosing the right timing, customer, offer, and channel, Predictive Marketing provides marketers the information they require in creating compelling, gainful campaigns.
Select the right customer
Primarily, the marketer chooses a suitable predictive model from Predictive Marketing’s depository, and uses it to decide which customers or customer section to aim. Arguably, the use of predictive models frequently considerably decreases the number of contacted customers, which results in measurable cost reductions. Consequently, Predictive Marketing characteristically decrease campaign costs by 25 to 40 percent, whereas maintaining or even mounting response rates.
Select the right channel
At this stage of the campaign procedure, marketers decide how best to contact each customer. Therefore, by employing each customer’s preferred channel, Metlife augments the response rates. Tentatively, Predictive Marketing allow marketing departments to optimize their outbound campaigns across channels, by choosing the most excellent channel for each customer (based on channel preferences as well as predicted response), balancing anticipated profits with the cost of the channel, and enchanting channel restraint into account. In the case that a channel arrives at capacity, for instance, Predictive Marketing shifts to a backup channel to guarantee completion of the campaign (Edwin, 1955).
Select the right time
Consumers presently have numerous choices for meeting their requirements. This attributes the reason to reach customers in an appropriate manner when their behavior designates an unmet requirement or a risk of defection or attrition. Therefore, Predictive Marketing repeatedly scans customer databases for just such events, and activates precise campaigns when a need or risk approaches. This event-marketing advance can result in as much as twofold the typical response rate. Arguably, the company may augment the frequency of campaigns to progress the chances of reaching customers at an ideal time. Moderately offering a convinced product once each year, they scurry campaigns for that product every week. As a result, these campaigns center on fewer customers, but the customers they docenter have a high response probability. Predictive Marketing enables marketers to table recurring campaigns, and to use predictive models and event triggers to choose the suitable customer targets.
Predictive Marketing scales to scuttle hundreds or even thousands of campaigns annually.
Select the right offer
In the case that Mitlife augments the number of campaigns they handle, they risk estranging their customers by overloading them with presents. Conservative campaign management tools are not designed to handle the possible overlap. Predictive Marketing, nonetheless, diminish this risk all the way through a comprehensive campaign optimization process. The process commences by making the customer—not the campaign—the center. For each customer, Predictive Marketing assesses all of the obtainable campaigns and chooses the one that most excellent balances the customer’s probability to react with the profit prospective of the campaigns.
Increasing marketing productivity
With retrospect with Mitlife Predictive Marketing not only assists companies augment response and profits, it assists marketing departments become more productive. Thus, the output enhancements result from two significant capabilities. To begin with, the sleek campaign creation, as well as, execution process permits marketers to send more campaigns in less time. Therefore numerous companies are able to reduce their campaign creation time from a few days to a few hours. Consequently, the increased output assists Metlife to realize better results lacking increasing personnel and resources, and presents marketers more time to extend new campaign concepts.
The second output enhancement is the marketer’s aptitude to optimize campaigns without help from statisticians. As a result, this eliminates the probable for bottlenecks, which happen when statisticians cannot keep up with the volume of campaigns(Edwin, 1955). For that reason, Predictive Marketing puts the tools and capabilities that marketers require factually at their fingertips, so they can attain significant marketing campaign results in less time.
Optimizing interactions across all channels
In addition to Predictive Marketing, SPSS Inc. present predictive analytic applications that mechanize and optimize your whole customer interaction procedure, from marketing to sales and services. Thus, these applications put together across channels and processes in the prediction and efficiently react to customer needs, preferences, and behaviors. For instance, the Predictive Call Center application facilitates inbound customer service calls into sales occasion, offering a new revenue-producing channel. Through execution predictive applications in the inbound and outbound call centers, direct mail operations, as well as Web site, use instantaneous improvements in customer answer and happiness, and increased revenues across these channels. Therefore the applications influence the existing databases, systems, and processes to deliver results rapidly and produce measurable business value.
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