Huawei Market Leadership in 5G, and Pricing Strategy for 5G Offerings

Huawei and 5G Networks

Huawei is now the world’s biggest telecommunication equipment maker, with a 28 per cent market share and more 5G contracts around the world than any other company.  Its closest rivals are Ericsson and Nokia, the European companies.  In US, Verizon is just beginning developing 5G networks (Kalyanaram 2019)

To maintain its market leadership and to increase its autonomy in 5G market, Huawei has released a series of 5G based chipsets designed to compete with U.S. and Korean competitors. These chipsets cover most of the telecom field: Kirin 980 chipset for smartphones; Balong 5000 chipset for modems; Tiangong 5G base station; and Kunpeng 920 chipset for the Taishan cloud server (Kalyanaram 2019.)

FairPay Architecture

What should be the Pricing Strategy for 5G offerings and services?  As proposed by Bertini and Reisman (2013), the customer should determine the value of the service and determine the price.  This is an iterative process, where the price is set on relational basis and not on transactional basis.  If the customer-determined price is not viable for the offering the service, then the firm can withdraw the offering.  Called as FairPay architecture, Bertini and Reisman describe the approach as follows (Reisman and Bertini 2018.)

As described below, there are three major elements to this pricing approach.

1.      Empowerment of the customers by delegating to them the responsibility and opportunity to define value and set a price.  The customers are allowed to experience the offering without any pre-conditions and then set the price.

2.      Dialog between the firm and the customers about value of the offering, the benefits, and the level of enrichment of the customer.  Firms suggest reference prices to anchor a customer’s price offer and can provide reports to remind people of the value received. Customers are asked to justify the prices paid by indicating their reasons. Firms respond with counterarguments. Importantly, this dialog is structured for scalability and personalization through the use of modern choice architectures.

3.      Reputation measures a customer’s use of her/his responsibility. Fairness rating.  Choice architectures are then applied to segment customers in terms of fairness (and other attributes) and apply “carrots” (relating to product tiers, perks, etc.) to improve profitability or “sticks” (the threat to remove a customer’s price-setting privilege) to at least sustain it.”

Reference Price and Latitude of Price Acceptance

The FairPay architecture recognized the existence of a Reference Price.  Reference Price is the internal mental price which forms the basis for comparison with the observed price (Kalyanaram and Winer 1995).  Accordingly, a customer’s ask price in the FairPay architecture will be dependent on her/his framing and reference price.

Reference price has multiple conceptualizations. The most common conceptualization models reference price as a predictive price expectation that is shaped by consumers’ prior experience and current purchase environment (Kalyanaram and Winer 1995.)   The theoretical basis comes from adaptation-level theory which holds that people judge a stimulus relative to the level to which they have become adapted.  Thus, in a pricing context, the expectation-based reference price is the adaptation level against which other price stimuli are judged ( Mazumdar et. al. 2005.)

There are many internal and external cues that determine the reference price of a customer.  A customer’s personal experience, word-of-mouth, advertisement, promotion and other instruments play a critical role.  Reference price varies across consumers and groups of consumers.  There is individual and group heterogeneity.  The type of reference price a consumer uses and the effect of the reference price have been shown to vary across consumers, creating an opportunity for segmenting and targeting consumers on the basis of reference price (Mazumdar et. al. 2005).  Firms can manage and impact the reference price on a customer or a segment by direct and indirect suggestions, advertisement, promotions and other marketing instruments.

Research also shows that around the reference price, there is a region of price insensitivity.  That is small changes in price around the reference price are not noticed, and do not impact the probability of purchase.  This is also referred to as latitude of price acceptance (Kalyanaram and Little 1994.)  In their work, Kalyanaram and Little have shown that many factors impact the width of the latitude of acceptance, including reference price level, knowledge level and brand loyalty.  For instance, consumers with higher reference level demonstrate a wider width of latitude.  On the other hand, consumers with higher level of knowledge and interest in the service demonstrate a smaller width.  Finally, brand loyal consumers as expected tolerate a wider latitude.

Accordingly, firm that wants to increase the price should “nibble” at price increases.  That is, the increases must be small.  Preferably, small enough to be within the latitude of price acceptance which will mean that there will be no impact on the probability of purchase.  With the increase price, the reference price will also increase in the adaptive model.  As the reference price increases, price latitude will increase.  So, there is much benefit in small increments of price increases.

References

BERTINI, M., & REISMAN, R. November 18, 2013.  When Selling Digital Content, Let the Customer Set the Price. Harvard Business Review Internet, Retrieved from https://hbr.org/2013/11/when-selling-digital-content-let-the-customer-set-the-price

KALYANARAM, G. & LITTLE, J.D.C. (1994). An Empirical Analysis of Latitude of Price Acceptance in Consumer Package Goods. Journal of Consumer Research, 21, 408-418.

https://doi.org/10.1086/209407

KALYANARAM, G. 2019. Fifth Generation (5G) Networks, Emerging New Global Economic and Market Order, and Huawei. NMIMS Engineering and Technology Review, Volume 1, Issue 2, 6-8.

KALYANARAM, G. & RAGUVIR, G. 1988. Market Entry Strategies: Pioneers Versus Late Arrivals.  Strategy + Business. Retrieved from https://www.strategy-business.com/article/18881?gko=c8128

KALYANARAM, G., ROBINSON, W.T., & URBAN, G.L. 1995. Order of Market Entry: Established Empirical Generalizations Emerging Empirical Generalizations, and Future Research. Marketing Science, 14, Institute for Operations Research and the Management Sciences (INFORMS), G212–G221.

MAZUMDAR, T., RAJ, S.P. & SINHA, I. 2005. Reference Price Research: Review

and Propositions. Journal of Marketing, Vol. 69, 84–102.

REISMAN, R. & BERTINI, M. 2018. A novel architecture to monetize digital offerings. Journal of Revenue Pricing Management, Volume 17, Issue 6, 453-458. https://doi.org/10.1057/s41272-018-0143-3