2月 042016

Today’s retailers have access to vast stores of data that allow them to create the personalised retail experience that customers have come to expect. Used in the right way, analytics can be the key to bringing customers in through the door, building a better online experience, or simply helping weather slow periods by enabling faster, more efficient and nimble supply chain changes.CustomerDigitalMarketing

Interestingly, customer perceptions towards a brand can significantly change following Black Friday discounting and January sales. Pricing strategy can be confusing at the best of times. Given the option of £50 off on a high-value product compared to a 70 percent discount on a low value item: which is the better offer? Which deal do consumers trust, and why? More importantly, how does it impact the brand perception?

One thing’s for sure: brand loyalty and consumer trust are no longer guaranteed in today’s fast-changing retail environment. Retailers must find alternative ways to engage with consumers.

The dark art of forecasting

For years, retailers have collected customer data through loyalty cards, email marketing promotions, point-of-sales tills, as well as online browsing behaviour and purchase orders. What has changed over the years is the technology that allows retailers to analyse and understand data. The advent of technologies like Hadoop has enabled the development of advanced analytics solutions to produce more insightful and timely answers for retailers, which is of huge value during peak trading seasons.

Another interesting point here is that often the data collected spans the last three years of trading or more. This is traditionally used by store managers to forecast pricing and demand. Yet we know that outdated historical data, coupled with unpredictable external factors like weather conditions, regularly produce inaccurate forecasts.

As a result, retailers are still making predictions largely based on gut feel rather than objective insights. More often than not, they are also spontaneously reacting to competitors’ price cuts without carefully calculating their profits and loss potential.

Brands switch off

Another common retail pitfall is the effect of over marketing with a general blanket message. Unaware of the consequence of blasting out daily and sometimes twice-daily promotional emails in the hope of catching shoppers’ bargain hunting instincts, retailers are actually turning customers away. Consumers will easily unsubscribe from brands they previously loved, especially if they feel they are bombarded with irrelevant and unwanted product recommendations that only clog up their inboxes.

True personalisation demands an intimate understanding of the customer, and willingness from the individual to participate.

One of our retail customers has been using data analytics to deliver personalised product displays. These are highly targeted to improve conversion and avoid endless page scrolling. Instead of feeling like every other shopper each time a customer enters the site, they can now enjoy a genuinely personal engagement with the retailer. Behaviour is predicted and product recommendations are based on browsing, and a wealth of customer and transaction data.

With a more personalised customer experience, shoppers will be able to purchase products at the best price, while receiving the best in-store, online and post-sales service.

As retailers have better insights into customer behaviour and spending patterns, they will be able to personalise the experience and product recommendation. In turn, shoppers will feel they are receiving better deals and being looked after by the retailer. Some customers may also find themselves receiving extra benefits or upgrades through loyalty schemes, enhancing the overall experience with the brand.

SAS has worked for many years with dunnhumby, the power behind Tesco’s Clubcard. Today there is a need to market to customers across multiple channels. SAS has also worked with Callcredit Information Group, specialists in marketing services, analytics and data, to deliver a market leading omnichannel marketing and analytics service. This partnership has delivered a solution to ASDA. It provides an easy-to-use interface, with repeatable processes to enable organisations to deliver more campaigns – from simple to complex – in a shorter space of time.

Sail through the online retail revolution

On Black Friday alone, shoppers spent £1.1 billion with UK online retailers, setting a new internet retail record which underlines the online shopping revolution. The true impact of this year’s trading results are yet to be seen but one thing is certain – retailers can no longer rely on historical data alone to forecast pricing models or stock performance.

The cost for retailers can be drastic if they cannot accurately analyse the net profitability impact ahead of the peak trading period.

Big data enables retailers to more accurately forecast actual consumer demand, based on the very latest fashions and trends, as well as the timing and the likely location for that demand. For example, big data can be used as follows:

  • Analyse the data and visualise the demand forecast on a specific product in a specific store on a specific time and day of the week. This enables the retailer to ensure they get specific stock to that location.
  • Predict what else the customers might like to buy in the same transaction and personalise the product recommendations accordingly.
  • Predict the behaviour of customers by channel. For example, how customers might move between online browsing and in store purchasing, or their preference for home deliveries or Click & Collect.
  • Use demand insight to negotiate more effectively what the retailer buys from suppliers and when. Also, strategically plan for a more coherent and cheaper supply chain and transportation journey, allowing for external factors like extreme weather conditions.
  • Understand which segment of the customer demographics are most valuable to the business and devise a more effective way to nurture their spending and relationship with the brand.
  • Delight your customers by presenting them with product and brand recommendations which you already know they will want and like.

In principle, the knowledge of who wants what and when is an art form in retailing that is rooted in the golden era of advertising and knowing your customer. Retailers that have enhanced their skills in this area will continue to grow and prosper, which is why demand for data analytics technology will continue to grow over the coming years.

Find out more about how to make effective pricing decisions.

tags: big data analytics, brand loyalty, customer loyalty, pricing, retail, retail analytics

Unlock your retail potential with better pricing models and brand loyalty was published on Customer Intelligence.

1月 192016

Are you drowning in data? Do you feel overwhelmed -- or underwhelmed -- with the myriad of options available to deal with your data problems? (especially in the area of pricing?) It's the era of big data, and many retailers are discovering that hope is found through new technologies like event stream processing or Hadoop. However, as […]

Data readiness: Step one to a successful pricing journey was published on SAS Voices.

1月 152016

While 2015 was an unpredictable and often difficult year for many UK retailers, their customers have certainly prospered. The Christmas season, in particular, saw increased discounts for the fifth year in a row. This followed a period when changing weather patterns and price deflation had already offset predicted sales, both in-store and […]

Who were retail's winners and losers in 2015? was published on SAS Voices.

1月 182012
A standing room only crowd gathered at the NRF BIG Show on Monday afternoon in New York to hear Winn-Dixie VP Chris Vukich share how this $7 billion regional grocer has achieved a successful pricing strategy that allows them to: See the impact of each price change. Create what-if scenarios. Model [...]
1月 062009

There are some new financial functions in SAS9.2 Base, including 8 options pricing functions(formerly in SAS Risk Dimension). These functions can compute the price of both call and put options on different underlying assets (stock, futures, currency, and exchange asset), using the following models respectively:

Model Underlying Call Put
Black-Scholes model Stock BLKSHCLPRC BLKSHPTPRC
Garman-Kohlhagen model Currency GARKHCLPRC GARKHPTPRC
Margrabe model Exchange MARGRCLPRC MARGRPTPRC
  • BLACKCLPRC: calculates the call price for European options on futures, based on the Black model.
  • BLACKPTPRC: calculates the put price for European options on futures, based on the Black model.
  • BLKSHCLPRT: calculates the call price for European options, based on the Black-Scholes model.
  • BLKSHPTPRT: calculates the put price for European options, based on the Black-Scholes model.
  • GARKHCLPRC: calculates the call price for European options on currencies, based on the Garman-Kohlhagen model.
  • GARKHPTPRC: calculates the put price for European options on currencies, based on the Garman-Kohlhagen model.
  • MARGRCLPRC: calculates the call price for European options on exchange assets, based on the Margrabe model.
  • MARGRPTPRC: calculates the put price for European options on exchange assets, based on the Margrabe model.

For more,see SAS9.2 online help,Functions and CALL Routines by Category: Financial

Note: A good web site for options pricing with different models, http://www.montegodata.co.uk/

SAS金融函数(1):期权定价(new in SAS9.2 Base)

 未分类  SAS金融函数(1):期权定价(new in SAS9.2 Base)已关闭评论
11月 301999

以前贴过一份SAS9.1 Base的金融函数(23个。SAS9.1/ETS还有9个),惜乎没有展开来讲。现在SAS9.2 Base新增了一些有意思的金融函数(还有一些调整,比如,SAS9.1/ETS那9个金融函数都整合到SAS9.2 Base中去了),正好可以慢慢道来,从新增的期权定价函数开始(以前这些函数在SAS的风险管理软件Risk Dimension里面)。

SAS9.2 Base新增的这些定价函数(8个)都是计算欧式期权价格的,对看涨(call)期权、看跌期权(put)以及不同的期权类型(股票期权、期货期权、货币期权、交换期权),分别提供了以下四种模型:

Black-Scholes model,传统的股票期权定价模型,见Fischer Black and Myron Scholes (1973)
Black model,Black-Scholes model的扩展,针对期货期权,见Fischer Black(1976),所以该模型又称作Black-76
Garman-Kohlhagen model,外汇期权定价模型,见Mark Garman and Steven Kohlhagen(1983)
Margrabe model,交换期权定价模型,见William Margrabe(1978)


    Model,定价模型    Underlying,标的物     函数(Call,看涨)     函数( Put,看跌)
Black model Futures,期货 BLACKCLPRC BLACKPTPRC
Black-Scholes model Stock,股票 BLKSHCLPRC BLKSHPTPRC
Garman-Kohlhagen model Currency,货币 GARKHCLPRC GARKHPTPRC
Margrabe model Exchange,资产交换 MARGRCLPRC MARGRPTPRC

具体用法,见SAS9.2的在线帮助文档,Functions and CALL Routines by Category: Financial