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Technology Retail in the UK

Sector average score

White star

63

Knowledge & Expertise

62

Style of Communication

65

Speed of Resolving Problems

62

Valuing the Customer

65

Last quarter we had no outright leader in the Technology Retail in the UK table. This quarter, Apple is the sole occupant, taking the spot by itself for the first time ever recorded by the Customer Effort Index.

It’s been an impressive quarter too for John Lewis, which moves up into joint second and Argos, which takes joint-sixth alongside Dell.

Rankings

The Rankings

Performance Tracker

Performance Tracker

Rank

1

Knowledge & Expertise

68

Style of Communication

69

Speed of Resolving Problems

66

Valuing the Customer

70

2

Knowledge & Expertise

65

Style of Communication

68

Speed of Resolving Problems

66

Valuing the Customer

70

2

Knowledge & Expertise

66

Style of Communication

68

Speed of Resolving Problems

66

Valuing the Customer

69

4

Knowledge & Expertise

63

Style of Communication

64

Speed of Resolving Problems

64

Valuing the Customer

67

5

Knowledge & Expertise

61

Style of Communication

66

Speed of Resolving Problems

63

Valuing the Customer

67

6

Knowledge & Expertise

61

Style of Communication

63

Speed of Resolving Problems

57

Valuing the Customer

64

6

Knowledge & Expertise

58

Style of Communication

64

Speed of Resolving Problems

61

Valuing the Customer

63

8

Knowledge & Expertise

58

Style of Communication

65

Speed of Resolving Problems

58

Valuing the Customer

60

9

Knowledge & Expertise

56

Style of Communication

61

Speed of Resolving Problems

54

Valuing the Customer

58

Effort

i

X

Effort

The Overall Effort Score is calculated using modelling on the results recorded in each of the four pillars

68

67

67

65

63

61

61

60

58

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Knowledge & Expertise

i

X

Knowledge & Expertise

Having knowledgable customer service agents that can clearly explain details to customers impacts the score in the ‘Knowledge & Expertise’ pillar

68

65

66

63

61

61

58

58

56

Style of Commun-
ication

i

X

Style of Communication

How polite customer service staff are and whether they can communicate in a customers’ chosen language contribute to ‘Style of Communication’ 

69

68

68

64

66

63

64

65

61

Speed of Resolving Problems

i

X

Speed of Resolving Problems

A quick resolution and short holding times ensure a strong ‘Speed Solving the Problem’ score

66

66

66

64

63

57

61

58

54

Valuing the Customer

i

X

Valuing the Customer

Companies that make their customers feel valued and understand their needs perform well in the ‘Understanding & Valuing the Customer’ pillar

70

70

69

67

67

64

63

60

58

NPS

i

X

NPS

NPS scores can be used to compare and contrast with Effort for an additional perspective

28

38

5

8

49

-6

13

6

19

+

+

+

+

+

+

+

+

+

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Last updated: Jul 2025

Next update: Oct 2025

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Executive summary

  • How can Technology Retail brands differentiate in ‘Style of Communication’? Eight points make up the different between first and last in this table.
  • We say

    When it comes to ’Style of Communication’, politeness and the range of languages available to non-English speakers are the main contributors where brands can stand out. CX leaders within their businesses should ask themselves whether they’re doing all they can across human and automated agents to perform against this pillar.

How to use the Customer Effort Index

Customer Effort Scores for the quarter are presented in the rankings, ordered by highest Overall Effort Score first, to show which brand scored highest with its customers for that period. Individual brand ratings against the four pillars take us underneath that Overall Effort Score, with brand-specific explanatory analysis from the CEI Editorial team.

You can use the display above to toggle between score, ranking and individual pillars. Clicking 'Performance Tracker'  enables you to analyse the performance over time.

The Customer Effort Index is calculated using a self-referential scale that is recomputed every wave based on what the minimum and maximum values are. This enables us to see how each brand is performing compared to the others within the data set. See About for more on the methodology. 

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