Pulse


Behavioural Data XTREMEPUSH


Xtremepush: show me you know me


Tommy Kearns, CEO of Xtremepush, explains the significance of player behaviour data to unlock sophisticated campaigns that can retain, reactivate, and reward players.


What exactly is the role of AI in CRM systems? How is this changing and in terms of sports betting and online gaming, who benefits?


AI has been part of sophisticated CRM systems for some time now, although the role that it plays has been rapidly evolving, particularly over the last twelve to eighteen months. Te use of machine learning models for the purpose of prediction and segmentation came first, with many platforms offering standardised approaches.


Te development of generative AI saw the technology added to CRM systems to help with use cases like subject line or content development. We are still in the early stages of this new development, and the impact on a CRM team’s performance or efficiency is still to be fully understood.


As we move into 2024, we believe that the application of machine learning will evolve, driven by an improved understanding of the topic within end users, combined with the growth of legislation around the use of AI, particularly in sensitive areas like betting. We build our machine learning products with flexibility and transparency at their core in anticipation of this change in demand - no longer will smoke and mirror black box methods be as widely accepted when it comes to the use of machine learning models. End users will understand that algorithms are not just one size fits all, and that is something we are leaning into here at Xtremepush.


If we think about who benefits, there are two


Tommy Kearns CEO Xtremepush


categories of impact that come to mind. When implemented correctly, machine learning models can improve the performance of CRM campaigns, especially from a targeting and bonus spend control POV. Generative AI on the other hand can streamline workflows and drive efficiency in process, but in our opinion that is yet to be truly realised in a tangible way.


What does hyper-localised marketing look like?


For sports betting and iGaming, hyper-localised marketing is a strategy that presents players with promotions that are relevant to their region. If you take the United States, for example, a sports bettor in Boston is far more likely to take up a promotional player prop opportunity on a Patriots star compared to someone based in New Jersey. When you layer this with player preferences such as their preferred market you can deliver an enhanced player experience.


When implemented correctly, machine learning models can


improve the performance of CRM campaigns, especially from a targeting and bonus spend control POV. Generative AI on the other hand can streamline workflows and drive efficiency in process, but is yet to be truly realised in a tangible way.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136  |  Page 137  |  Page 138  |  Page 139  |  Page 140  |  Page 141  |  Page 142  |  Page 143  |  Page 144  |  Page 145  |  Page 146  |  Page 147  |  Page 148  |  Page 149  |  Page 150  |  Page 151  |  Page 152  |  Page 153  |  Page 154  |  Page 155  |  Page 156  |  Page 157  |  Page 158  |  Page 159  |  Page 160  |  Page 161  |  Page 162  |  Page 163  |  Page 164  |  Page 165  |  Page 166  |  Page 167  |  Page 168  |  Page 169  |  Page 170  |  Page 171  |  Page 172  |  Page 173  |  Page 174  |  Page 175  |  Page 176  |  Page 177  |  Page 178  |  Page 179  |  Page 180  |  Page 181  |  Page 182  |  Page 183  |  Page 184  |  Page 185  |  Page 186  |  Page 187  |  Page 188  |  Page 189  |  Page 190  |  Page 191  |  Page 192  |  Page 193  |  Page 194  |  Page 195  |  Page 196  |  Page 197  |  Page 198  |  Page 199  |  Page 200  |  Page 201  |  Page 202  |  Page 203  |  Page 204  |  Page 205  |  Page 206  |  Page 207  |  Page 208  |  Page 209  |  Page 210  |  Page 211  |  Page 212